How to Program Connect 4 in Python! (part 3) - Background Graphics Keith Galli 2026-09-17 | How to Program Connect 4 in Python! (part 3) - Background Graphics
Advanced Web Scraping Tutorial! (w/ Python Beautiful Soup Library) Keith Galli 2024-06-08 | Get started w/ Bright Data + $15 free credit using this link!brdta.com/keithgalliIn this video, we're diving into advanced web scraping techniques with Python. If you haven't seen my overview of the Beautiful Soup library, check it out first for some foundational knowledge. Web scraping is a highly valuable skill, especially for freelance work. This tutorial will take you through sophisticated scraping methods, using Walmart as an example.Before we start, a big thank you to our sponsor, Bright Data. They offer proxy tools that make advanced web scraping much easier, allowing you to bypass restrictions set by websites. Check out their data sets marketplace for quick access to various data.In this video, we'll cover:- Setting up and understanding the HTML structure of a web page- Extracting data using Beautiful Soup and handling dynamic content- Implementing headers to avoid detection- Parsing JSON data for efficient scraping- Using proxies with Bright Data to bypass IP blocking- Error handling and retries in scraping- Storing scraped data and handling multiple search queriesIf you need help getting started with web scraping, check out my original tutorial on BeautifulSoup:youtu.be/GjKQ6V_ViQE?si=f9Xo0ING4fNLhLx2Helpful Links:GitHub Repository with Code Examples: github.com/KeithGalli/advanced-scrapingVideo Timeline!0:00 - Intro & Overview1:30 - Identifying HTML Structure for Scraping (from Walmart)4:26 - Writing Python BeautifulSoup Code to Extract Info from Walmart.com7:22 - Implementing modified request headers to avoid detection6:10 - Handling Dynamic Content8:00 - Implementing Modified Request Headers to Avoid Detection (look more human when scraping)9:30 - Parsing Complicated JSON Data (Using LLMs to help)15:28 - Extending our Code to Collect Info on Many Products (Automating Search)24:45 - Improving our Code (avoiding duplicates, multiple search terms, using a queue, etc.)27:20 - Setting Up Proxies with Bright Data (Get around IP Address blocks)36:35 - Error Handling and Retries39:36 - Automating actions on pages with Selenium 41:42 - Conclusion & Next StepsI hope you find this tutorial useful. If you did, please give it a thumbs up and subscribe to the channel for more tutorials. Let me know in the comments how you plan to use these web scraping techniques in your projects. Enjoy scraping!-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Real-World Dataset Cleaning with Python Pandas! (Olympic Athletes Dataset) Keith Galli 2024-04-20 | I'm prepping a dataset for an upcoming tutorial and I figured walking through the process of cleaning it would work well for a livestream! We use various Python Pandas functions to accomplish our data cleaning goals. We'll be working off of this repo: github.com/KeithGalli/Olympics-DatasetSome topics that we cover:- How you can use web scraping to collect data like this (Python beautifulsoup).- Splitting strings into separate columns- Using regular expressions (regexes) to extract specific details from columns- Converting columns to datetime & numeric types- Grabbing only a subset of our columns Sorry that this was a bit last minute scheduling-wise, will try to give more advance notice in the future!Video timeline!0:00 - Livestream Overview4:00 - About the Olympics dataset (source website and how it was scraped)9:50 - Cleaning the dataset (getting started with code & data)19:26 - What aspects of our data should be cleaned?29:08 - Get rid of bullet points in Used name column34:08 - How to split Measurements into two separate height/weight numeric columns.1:05:00 - Parse out dates from Born & Died columns1:25:43 - Parse out city, region, and country from Born column (working with regular expressions)1:41:15 - Get rid of the extra columns 1:46:08 - Next steps (how would we clean the results.csv)1:49:41 - Questions & Answers ------------------------- Follow me on social media! Instagram | instagram.com/keithgalli Twitter | twitter.com/keithgalli TikTok | tiktok.com/@keithgalli ------------------------- Practice your Python Pandas data science skills with problems on StrataScratch! stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Solving 100 Python Pandas Problems! (from easy to very difficult) Keith Galli 2024-04-13 | In this tutorial, you'll gain hands-on experience with the python pandas library, building experience with data manipulation and analysis skills important for data science. You'll learn how to create, modify, and analyze DataFrames, handle missing data (NaNs), clean messy data, and generate some visualizations. By tackling a variety of problems, from basic data handling to advanced DataFrame techniques, you'll build a solid foundation in managing and interpreting real-world data sets using pandas.Repo we're working off of (credit to Alex Riley who put repo together):github.com/ajcr/100-pandas-puzzlesMy code solutions (use repo above for blank starting template):github.com/KeithGalli/100-pandas-puzzlesHope that you enjoy this video. If you do, make sure to like it and subscribe to not miss future videos like this!Video Timeline!0:00 - Intro & Setup2:14 - Problems (1-3) Initial pandas setup4:42 - Problems (4-10) DataFrame operations4:52 - 4) Create a dataframe from dictionary5:24 - 5) Display dataframe summary5:41 - 6) First 3 rows of the dataframe6:02 - 7) Select ‘animal’ and ‘age’ columns7:42 - 8) Data in specific rows and columns9:06 - 9) Rows with visits greater than 39:57 - 10) Rows with NaN in age10:56 - 11) Cats younger than 3 years11:35 - 12) Age between 2 and 412:45 - 13) Change age in row ‘f’15:56 - 14) Sum of all visits16:41 - 15) Average age by animal20:21 - 16) Modify and revert rows24:06 - 17) Count by animal type25:28 - Quick review26:17 - 18) Sort by age and visits28:07 - 19) Convert 'priority' to boolean29:42 - 20) Replace 'snake' with 'python'30:53 - 21) Mean age by animal and visits33:49 - Advanced DataFrame techniques33:57 - 22) Filter duplicate integers43:18 - 23) Subtract row mean45:42 - 24) Column with smallest sum50:39 - 25) Count unique rows53:17 - 26) Column with third NaN1:10:27 - Solution review for 261:17:13 - 27) Sum of top three values1:24:01 - 28) Sum by column condition1:40:11 - Recent problem review1:42:53 - 29) Count differences since last zero1:56:19 - 30) Locate largest values2:08:38 - 31) Replace negatives with mean2:17:43 - 32) Rolling mean over groups2:23:10 - Series and DatetimeIndex2:23:12 - 33) DatetimeIndex for 20152:27:56 - 34) Sum values on Wednesdays2:45:04 - 35) Monthly mean values2:46:16 - 36) Best value in four-month groups2:50:26 - 37) DatetimeIndex of third Thursdays2:59:03 - Cleaning Data2:59:40 - 38) Fill missing FlightNumber3:02:45 - 39) Split column by delimiter3:06:47 - 40) Fix city name capitalization3:08:30 - 41) Reattach columns3:13:11 - 42) Fix airline name punctuation3:17:45 - 43) Expand RecentDelays into columns3:27:31 - MultiIndexes in Pandas3:27:34 - 44) Construct a MultiIndex3:30:37 - Solution review3:32:44 - 45) Lexicographically sorted check3:32:58 - 46) Select specific MultiIndex labels3:34:23 - 47) Slice Series with MultiIndex3:35:24 - 48) Sum by first level3:37:47 - 49) Alternative sum method3:40:08 - Additional solution insights3:41:22 - 50) Swap MultiIndex levels3:45:27 - Minesweeper problems3:45:44 - 51) Generate coordinate grid4:00:28 - 52) Add 'safe' or 'mine' column4:03:04 - 53) Count adjacent mines4:27:33 - Review solution to 534:33:02 - Skipped problems 54 & 554:33:11 - Plotting4:33:12 - 56) Scatter plot with black x markers4:41:26 - 57) Plot four data types4:52:50 - 58) Overlay multiple graphs5:03:11 - 59) Hourly stock data summary5:14:12 - 60) Candlestick plot------------------Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keith
Ask me anything! (data science, LLMs, landing a job, and more) Keith Galli 2024-04-07 | I want to start live streaming on this channel! Figure an AMA is a good way to start. If you have any questions before the live stream starts, leave them in the chat/comments :) Video timeline!0:00 - Intro & Live Stream Overview4:58 - How over saturated is the data science job market and will things improve in your opinion?7:26 - How much maths is needed to get a data science job?9:55 - Can you share a basic roadmap to learn generative AI and LLMs?13:08 - What future-proof tech career to should someone focus on who’s looking to change career?16:52 - Is data structures & algorithms (dsa) necessary to get a job in data science?19:17 - How to get good at data structures and algorithms?22:20 - Why don’t you make videos regularly now?24:18 - How much do you need to know for entry-level roles / college internships?27:02 - How important is domain knowledge for data science?29:29 - Amazon’s AI-based ‘just walk out’ retail checkout tech controversy thoughts32:30 - Any good data projects to increase visibility to companies?36:05 - Do you think we should all learn vector databases?39:10 - Is webscraping illegal? what can I do and not do?43:14 - What are you working on at the moment?45:25 - How can I turn a financial database I’m building into an interesting portfolio project to showcase work?49:23 - What advice do you have for data scientists who want to get into freelance/consulting?55:15 - What are important skills for DS beyond ML & AI?59:42 - Do I need to become a full-stack programmer to have success in this field?1:02:31 - If you weren’t allowed to do programming or create content, what would you do?1:03:39 - How did you achieve your advanced height? Asking for a friend.1:04:23 - Final thoughts. Thanks for coming!------------------------- Follow me on social media! Instagram | instagram.com/keithgalli Twitter | twitter.com/keithgalli TikTok | tiktok.com/@keithgalli ------------------------- Practice your Python Pandas data science skills with problems on StrataScratch! stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - patreon.com/keithgalli *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Keith Galli Live Stream Keith Galli 2024-04-05 | -------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Solving Real-World Data Science Problems with LLMs! (Historical Document Analysis) Keith Galli 2024-03-20 | In this video we walk through the process of analyzing historical documents using Python & Large Language Models. We start by setting up LLMs using both closed-source (OpenAI API) and open-source (Llama 2 via Ollama) options. Next, we walk through how we can leverage the LLMs to parse out entities from text. After this we actually start playing around with our data, loading in a specific subcategory of documents from Kaggle and see how we can connect pages from the same documents together. Once this is completed, we repeat the entity parsing process for our actual data to get pieces of information such as names, ages, and locations from our documents. Finally we analyze these entities to learn some insights from our document database.Kaggle Dataset: kaggle.com/datasets/keithgalli/freedmens-bureau-historical-documentsGitHub Repo: github.com/keithgalli/historical-docs-analysis Project Website: freedmensbureau.info Contributors:Abdessalem Boukil (NLP Research & Analysis): linkedin.com/in/abdessalem-boukil-37923637Trent Self (Kaggle Dataset Setup): linkedin.com/in/trentonselfIf you enjoyed this project video, make sure to throw it a thumbs up & subscribe! Let me know in the comments if you have any questions. It would also be helpful for people to upvote the Kaggle dataset for visibility!---------------------------Video timeline!0:00 - Video Overview & Reference Material3:05 - Data & Code Setup5:04 - Task #0: Configure LLM to use with Python (OpenAI API)20:10 - Task #0 (continued): LLM Configuration with Open-Source Model (LLama 2 via Ollama)27:39 - Task #1: Use LLM to Parse Simple Sentence Examples41:22 - Sub-task #1: Convert string to Python Object44:29 - Task #1 (continued): Use Open-Source LLM to Parse Sentence Examples w/ LangChain56:24 - Quick note on a benefit of using LangChain (easily switching between models)58:06 - Task #2 (warmup): Grab Apprenticeship Agreement rows from Dataframe1:06:22 - Task #2: Connect Pages that Belong to the Same Documents1:56:36 - Task #3: Parse out values from merged documents2:12:44 - Task #4 (setup): Analyze Results2:17:52 - Fixing up our results from task #3 quickly2:20:41 - Task #4: Find the average age of apprentices in our merged contract documents 2:30:59 - Other analysis, wlho had the most apprentices?-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to make your GitHub more impressive to Employers! (5 simple tips) Keith Galli 2024-02-26 | In this video, we look into how you can enhance your GitHub profile to help catch the eye of potential employers. With insights from my experience in hiring for data science and programming roles, we walk through five actionable tips to elevate your GitHub presence. These range from showcasing your coding projects effectively to optimizing your profile's appearance and readability. Whether you're a novice or an experienced coder, these strategies will help you stand out in the competitive job market by demonstrating your ability to build impressive projects with code. Check out Bobby's GitHub!github.com/bobbyg603Python API video Mentioned:youtu.be/moi8WPO3Xhs?si=GYQenpZ_ASE8BNqfLadder's Study on 7 Second Resume Review:theladders.com/career-advice/you-only-get-6-seconds-of-fame-make-it-count0:00 - Introduction1:50 - Tip 1: Show Private Repository Activity2:57 - Tip 2: Highlight best work using pins4:13 - Tip 3: Create a Profile README11:32 - Tip 4: Fill in all Profile Details13:45 - Tip 5: Fill in READMEs on highlighted repos-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Can You Solve These 3 Data Analysis Puzzles? (AnalystBuilder & Python Pandas) Keith Galli 2023-12-27 | Check out AnalystBuilder!analystbuilder.com/?via=keithJoin me as I dive into the Analyst Builder platform created by fellow YouTuber @AlexTheAnalyst! In this video, we tackle a series of Python programming challenges, demonstrating real-time problem-solving and coding skills. We navigate through various tasks, from identifying high-risk heart attack patients using data analysis to manipulating strings and solving complex data queries. Whether you're a seasoned programmer or just starting, this video offers insights into Python programming and practical data analysis techniques.Highlights- Overview of Analyst Builder platform and its user-friendly interface.- Coding session solving Python problems related to data analysis and manipulation.- Detailed walkthrough of three distinct challenges: heart attack risk assessment, customer data anonymization, and sales data analysis for a bakery contest.Python & Programming Skills Used:- Writing efficient Python queries to extract specific data from data frames.- Utilizing Pandas for data analysis, including conditional filtering, sorting, and grouping.- Implementing string manipulation techniques for data anonymization.- Leveraging pivot tables for comparative analysis of sales data.- Dynamic problem-solving and debugging using Python in a real-world scenario.If you have any questions, let me know in the comments!If you enjoyed this video, make sure to throw it a like & subscribe for all future content :)Video timeline!0:00 - Overview & Getting Started0:50 - 1. Predicting Heart Attack Risk (Easy Problem)6:44 - 2. Data Anonymization (Medium Problem)11:53 - 3. Dessert Duel (Hard Problem)Keywords: #Python #Pandas #DataAnalysis #AnalystBuilder #ProblemSolving #Coding #Tutorial #DataScience -------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Python Project: Implement a REST API with Flask & Flasgger Libraries! Keith Galli 2023-12-03 | We continue where we left on in the last video and implement two REST endpoints for a book review API. We implement a Get request to retrieve all our reviews and implement a Post request to add a new review to our Airtable database. We use the Flasgger library to create interactive documentation for our endpoints. We use ChatGPT to help us write our endpoints Python code. We use Render.com to deploy our API to a publicly accessible URLLink to first video:youtu.be/moi8WPO3XhsSource code:github.com/KeithGalli/python-api-example/tree/part1-codeFinal source code:github.com/KeithGalli/python-api-example/tree/book_reviewGitlog command info can be found in this article:medium.com/posh-engineering/mastering-the-terminal-to-improve-development-speed-fd234149efb6 How to set environment variables (all operating systems:twilio.com/blog/how-to-set-environment-variables-html More information on the Flasgger library:github.com/flasgger/flasgger Handling API keys with a Python decorator:coderwall.com/p/4qickw/require-an-api-key-for-a-route-in-flask-using-only-a-decoratorIf you enjoyed this video, be sure to give it a like and subscribe :). If you have any questions, let me know in the comments!Video timeline!0:00 - Overview0:41 - Getting started on the Book Review API2:20 - Set up Airtable as our database & connect to it with Python10:44 - Implement code to add reviews and view all reviews31:40 - Adding a POST request to our API36:40 - Trying out our new endpoints (using documentation & requests library of Python)41:32 - Commit code to Github & deploy live to Render.com-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to create & deploy an API in Python! (with interactive documentation) Keith Galli 2023-11-26 | In this video, we work through the process of creating and deploying a Python3 based API using libraries such as Flask, Flask-Restful, & Flasgger.Link to code: github.com/KeithGalli/python-api-examplePart 2: youtu.be/rCrDYRBOuNw?si=bBeT9orOpjr-089SIn this first video, we start with the basics, setting up GET endpoints and learning how to deploy our API to the cloud (using Render.com). This part is crucial for understanding the core functions of APIs and making them accessible worldwide.In the second video, we progress to implementing POST requests and constructing a full-fledged Book Review API, similar to services like Goodreads. This involves integrating API functionality to post and retrieve book reviews from a database, with practical examples using Airtable for database interaction.Flasgger plays a crucial role in our tutorial, providing interactive documentation through its SwaggerUI integration. This feature allows you to visualize and interact with your API directly at http://localhost:5000/apidocs, offering a clear and user-friendly overview of your API's capabilities.Additionally, we cover essential practices like working with GitHub repositories, managing Python libraries, and employing Flask and Flask-Restful for API development. The skills in crafting effective GET and POST requests are emphasized, highlighting the importance of efficient system communication.This video is ideal for anyone keen on enhancing their Python programming, API development, and cloud deployment skills. Whether you're a beginner or an experienced developer, this tutorial provides valuable experience in developing well-documented and user-friendly APIs.Video timeline!0:00 - Video overview1:18 - What we're building3:20 - How to get setup with Github template code7:00 - Taking a look at the Flask, Flasgger Python3 code8:38 - Testing some API requests (GET) locally13:09 - Building another GET request endpoint (with multiple parameters)14:34 - Using ChatGPT to help us build another endpoint22:43 - Deploying our API to a live public URL endpoint (using render.com)-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Complete Regular Expressions Tutorial! (with exercises for practice) Keith Galli 2023-04-13 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video we go through all the fundamentals of using regular expressions (regexes) to match patterns in programming. In this video we cover the following:- Character Sets [a-zA-Z0-9]- Quantifiers *, +, ?, {3,5}- Metacharacters ^ . | $- Character Classes \b \s \w \d- Groups- Lookahead & Lookbehind assertions- and more!There is a detailed timeline down below. In a future video we will apply what we learn in this one to the Python programming language (re library) -- subscribe to not miss that!Resources!Source code & txt files: github.com/keithgalli/regular-expressionsPractice exercise video: youtu.be/Qv_RYpREz5kCheat sheet: cheatography.com/davechild/cheat-sheets/regular-expressionsRegex golf: https://alf.nu/RegexGolfJavascript Info: javascript.info/regexp-lookahead-lookbehind-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video timeline!0:00 - Video overview & topics covered1:43 - Basic regex syntax (building up an intuition)4:23 - Character Sets Overview ([A-Za-z0-9])5:57 - Quantifiers Guide (*, +, ?, {3,5})9:30 - Guided Exercise: Find all words that don't use vowels11:08 - Helpful cheat sheet to remember regex syntax in the real-world12:47 - Matching words/patterns of a specific length ({3,5})14:58 - OR operator overview17:14 - Guided Exercise: Match valid sentences (starts with capital letter, ends with period)21:18 - Character classes overview (\w, \b, \d, \s)23:13 - Escaping Characters25:02 - Practice Exercise #1: Write a regular expression to match meme text format30:39 - Practice Exercise #2: Write a regular expression to match a specific date format39:03 - Groups overview50:16 - Lookahead & Lookbehind Assertions1:00:18 - Practice Exercise #3: Detect if same word pops up multiple times in a sentence1:06:04 - Practice Exercise #4: Password matching with rules1:16:16 - Some final recommendations! (additional practice, chatgpt, etc.)
Full Data Science Mock Interview! (featuring Kylie Ying) Keith Galli 2023-01-09 | Check out Mobile Pixels! bit.ly/3WKUC55In this video we walk through a full-length data science interview. The task in the video is to develop a model to identify bots on a social media platform. In the video we cover topics including feature vectorization, one-hot encodings, dataset building, and more!Check out Kylie's channel: @KylieYYing -------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video timeline!0:00 - Video overview & format3:38 - Introductory Behavioral questions | Data science interview9:11 - Social media platform bot issue task overview | Data science interview16:51 - What are some features we should investigate regarding the bot issue? | Data science interview26:27 - Classification model implementation details (using feature vectors) | Data science interview43:03 - What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview53:03 - Technical implementation details (python libraries, cloud services, etc) | Data science interview57:26 - Any questions for me? | Data science interview1:05:07 - Post-interview breakdown & analysisThank you to mobile pixels for sponsoring this video!
Full Python Portfolio Project! Create a smart program to download & transcribe top podcasts. Keith Galli 2022-11-22 | Check out assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=keithgalli to start transcribing as many podcasts as your heart desires!In this video we create a Python program that can automatically scrape the RSS feeds of your favorite podcasters, pulling out the episodes you’ll find most interesting, and downloading + transcribing them.This project leverages a wide range of Python skills making it a good portfolio project. In it you’ll use the BeautifulSoup + requests libraries to first web scrape & download MP3 podcast files. You can use the regex library (re) and other NLP libraries to smart search for specific episodes that you'll enjoy. Next you'll see how you can use AssemblyAI's speech to text API to transcribe all of the episodes that you download. This code will be leveraged to create a text corpora that is going to be used in language analysis in upcoming tutorials.To view/download source code for project: github.com/keithgalli/podcast-downloaderIf you have any questions, let me know in the comments!Make sure to smash like + subscribe if you enjoyed this video :)-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video timeline!0:00 - Video Introduction1:19 - How podcasts work (RSS feeds overview)5:11 - How can we utilize the XML webpages? (breakdown of RSS feed information & how we’ll use it to create a smart program)7:47 - Accessing this project on GitHub9:22 -Writing Python code to download podcasts locally (requests & beautifulsoup libraries)18:10 - Modify our script to be able to download many podcasts22:51 - Building in smart search capabilities to grab podcasts we’ll find most interesting!31:00 - Using the AssemblyAI API to transcribe the podcasts we’ve downloaded1:06:08 - Cleaning our code with functions & classes and putting everything into Python scripts.1:18:09 - Portfolio project extension ideas! (Spotify API, NLP semantic search)1:19:56 - Smash like & subscribe pretty please :)
Solving Real-World Data Science Interview Questions! (with Python Pandas) Keith Galli 2022-07-26 | Visit brilliant.org/KeithGalli to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscriptionIn this video we solve a series of Data Science Interview questions on Stratascratch. We start with easy problems using Python Pandas and then progressively get more difficult. At the end of the video we do five non-coding interview questions that force you to think at a high level.Mentioned Resources!Second Channel: youtube.com/c/techtrekbykeithgalliRegex Cheat Sheet: cheatography.com/davechild/cheat-sheets/regular-expressionsProbability text book: amazon.com/dp/188652923X/ref=cm_sw_em_r_mt_dp_3JSVKBY80FQ3EEDGSRD6Here are the questions that we complete (in order)~~ Coding ~~1. Finding Updated Records: platform.stratascratch.com/coding/10299-finding-updated-records?code_type=2&via=keith2. Number of Bathrooms and Bedrooms: platform.stratascratch.com/coding/9622-number-of-bathrooms-and-bedrooms?code_type=2&via=keith3. Counting Instances in Text: platform.stratascratch.com/coding/9814-counting-instances-in-text?code_type=2&via=keith4. Customer Revenue in March: platform.stratascratch.com/coding/9782-customer-revenue-in-march?code_type=2&via=keith5. Monthly Percentage Difference: platform.stratascratch.com/coding/10319-monthly-percentage-difference?code_type=2&via=keith6. Premium vs Freemium: platform.stratascratch.com/coding/10300-premium-vs-freemium?code_type=2&via=keith~~ Non-Coding ~~1. Credit Card Activity: platform.stratascratch.com/technical/2342-credit-card-activity?via=keith2. Outliers Detection: platform.stratascratch.com/technical/2372-outliers-detection?via=keith3. Probability of Having a Sister: platform.stratascratch.com/technical/2368-probability-of-having-a-sister?via=keith4. Uber Black Rides: platform.stratascratch.com/technical/2305-uber-black-rides?via=keith5. Terabyte of Data: platform.stratascratch.com/technical/2364-terabyte-of-data?via=keithThe skills that we work on in this video include:- Python Pandas- Groupby & Aggregate DataFrames- Use regexes to analyze text- Datetime objects in Pandas- Filtering by Conditionals- Applying a lambda function to a data frameIf you have any questions, let me know in the comments!If you enjoyed this video, make sure to throw it a like & subscribe for all future content :)-------------------------Video Timeline!0:00 - Intro & Video Overview0:46 - Check out this Video’s Sponsor, Brilliant!3:10 - Coding #1 (Microsoft, Easy) - Finding Updated Records10:36 - Coding #2 (Airbnb, Easy) - Number of Bathrooms and Bedrooms16:38 - Coding #3 (Google, Medium) - Counting Instances in Text28:23 - Coding #4 (Meta/Facebook, Medium) - Customer Revenue in March36:51 - Coding #5 (Amazon, Hard) - Monthly Percentage Difference56:38 - Coding #6 (Microsoft, Hard) - Premium vs Freemium01:10:28 - Non-Coding #1 (Visa, Easy) - Credit Card Activity01:13:33 - Non-Coding #2 (IBM, Easy) - Outliers Detection01:16:46 - Non-Coding #3 (Google, Medium) - Probability of Having a Sister01:27:19 - Non-Coding #4 (Uber, Medium) - Uber Black Rides01:36:57 - Non-Coding #5 (Capital One, Hard) - Terabyte of Data01:46:41 - Video Conclusion & Recap-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.This video was Sponsored by Brilliant
5 Jupyter Notebook Tips & Tricks to Improve your Data Science Workflow! Keith Galli 2022-06-01 | Visit brilliant.org/KeithGalli to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscriptionIn this video we walk through some of my favorite tips & tricks for doing data science with Jupyter Notebooks. Many of these tips have helped me become more efficient writing Python code for my data science projects. Topics covered:- Running bash commands from a jupyter notebook. You can use “!” in a code cell to run any terminal command that you want. You can also try using the “%” to execute magic lines. This includes some of the same bash commands that you might gravitate to as well as some Jupyter specific commands.- The most useful shortcuts that I think you should know. Ctrl+enter to run a cell, shift+enter to run a cell and skip to the next, B to create new cells below, M to turn a cell to markdown, and several more. We will also look at find & replace in this section.- Changing pandas default display settings (number of rows to display, columns, decimal points, etc.)- Turning on OS alerts for running Jupyter Notebook cells. This is particularly useful if you have a long machine learning model training job going on and you want to know exactly when it finishes so that you can play around with the results.- Creating slideshows within a Jupyter notebook! Bonus topic:- Changing your Jupyter Notebook theme! Monokai is a good one to use if you like dark-themesSource code:github.com/KeithGalli/Data-Science-Tips/tree/master/jupyter-notebook-tipsIf you have any questions about these tips & tricks, or if you have additional recommendations, please let me know in the comments!I hope you enjoyed this video! Please subscribe & give it a like if you did.Resources:- towardsdatascience.com/8-commonly-used-pandas-display-options-you-should-know-a832365efa95Learn more about magic commands "%": ipython.readthedocs.io/en/stable/interactive/magics.html-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------Song at the endgood morning by Amine Maxwell soundcloud.com/aminemaxwellCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoYMusic promoted by Audio Library youtu.be/SQWFdnbzlgI-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.This video was sponsored by Brilliant.org-------------------------Video timeline!0:00 - Introduction & video overview0:25 - Shoutout to this video’s sponsor, Brilliant.org!2:11 - 1. Running terminal commands such as “pip” directly in a notebook5:17 - Magic lines in Jupyter6:11- 2. Shortcuts that you need to know for Jupyter!9:25 - 3. Changing default Pandas options to improve results display14:00 - 4. Setting up notifications for when a cell finishes execution18:08 - 5. Creating slideshows from a IPython notebook!23:00 - Conclusion (and link to bonus tip!)
Solving real world data science problems with Python! (computer vision edition) Keith Galli 2022-05-11 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video we work on a real world computer vision problem using Python. The problem task is to create a model that can distinguish a flower known as “La Eterna” from other types of flowers.To do this we create convolutional neural networks (CNNs) using the Tensorflow/Keras libraries. We examine how to create a simple model and then improve it using techniques such as data augmentation & preprocessing. We play around with different types of network architectures and see how changes improve or decrease overall task performance.Link to source code (Github):github.com/KeithGalli/Unlocked_Challenge_4 Link to HP challenge:hp.com/us-en/workstations/industries/data-science/unlocked-challenge.html My previous videos on neural networks!Intro to neural nets: youtu.be/aBIGJeHRZLQReal-world tutorial: youtu.be/44U8jJxaNp8 *** I've left a bunch of additional useful resources in the README of the Github repo ***Videography for clips I integrated at the start by Ryan Cabanaryancabana.comHopefully you enjoy this video! Please leave it a like & subscribe if you did :).If you have questions about topics covered in this video, please let me know in the comments.-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------Song at the endgood morning by Amine Maxwell soundcloud.com/aminemaxwellCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoYMusic promoted by Audio Library youtu.be/SQWFdnbzlgI-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video timeline!0:00 - Intro 0:40 - Video overview (what we’ll be working on)1:53 - Code setup (GitHub repo & HP challenge link)5:11 - Exploring the dataset that we’ll be using6:20 - Reviewing template code (starter-code.ipynb)8:53 - Installing necessary Python libraries (opencv-python, tensorflow)10:31 - Reviewing template code (part 2)11:03 - How we load in the dataset (ImageDataGenerator, flow_from_directory)14:33 - Building our first classifier (convolutional neural net - CNN)25:19 - Methods to improve neural network performance (MaxPooling, dropout, network architecture)29:30 - Quick discussion about importance of precision & recall versus accuracy32:35 - Data augmentation & preprocessing (another way to improve performance)47:15 - Programmatically finding the best neural network architectures (Keras Tuner)1:20:00 - Video recap & conclusion
Want to land your first data science role??... I have a BIG announcement! (no degree required) Keith Galli 2022-05-02 | Apply to the Program!goto.masterschool.com/8semynI'm excited to announce the launch of my data analytics bootcamp in June! This program is 7-months long and is designed to help you land your first role in tech. In the program we will cover tools such as Tableau, SQL, & Python as well as topics such as A/B testing, version control (with Git), and machine learning.This program is offered through Masterschool. I will lead the program as the head mentor, but there will also be teaching assistants, career counselors, and other staff to help you every step of the way.About Masterschool:"Masterschool is where talented individuals build their careers in tech. When you join our network of success-based schools, you learn from the experts exactly what the industry needs, we actively work to help you find a job, and you pay nothing until you are hired."If you have any questions, let me know in the comments!!Thank you to Ryan Cabana for the videography!ryancabana.comIf you enjoyed the video, make sure to leave a like & subscribe :)-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTikTok | tiktok.com/@keithgalli-------------------------SongHurricane - Halsey (Artsy Remix)Start: youtu.be/L7yGsIQtbXEEnd: youtu.be/H88a_7bI79E-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video timeline!0:00 - Intro0:46 - The announcement!4:03 - Some final notes & how to apply
Complete Natural Language Processing (NLP) Tutorial in Python! (with examples) Keith Galli 2022-03-17 | In this video we go through the major concepts in natural language processing using Python libraries! We use examples to help drill down the concepts. There is content in this video for all skill levels (beginners to experts). I originally recorded this video for the PyCon Conference.GitHub repo: github.com/KeithGalli/pycon2020Patreon: patreon.com/keithgalliYT Membership: youtube.com/c/KGMIT/membershipSome of the topics we cover:- Bag-of-words- Word vectors- Stemming/Lemmatization- Spell correction- Transformer Architecture (Attention is all you need)- State of the art models (OpenAI GPT, BERT)Some of the libraries used:- sklearn- spaCy- NLTK- TextBlobHope you enjoy & let me know if you have any questions! Make sure to subscribe if you haven't already :).-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliIf you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Song at the endgood morning by Amine Maxwell soundcloud.com/aminemaxwellCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoYMusic promoted by Audio Library youtu.be/SQWFdnbzlgI-------------------------Video Timeline!~~ NLP Fundamentals ~~0:00 - Announcements!1:12 - Video overview & timeline3:06 - Bag of words (BOW) overview4:42 - Bag of words example code! (sklearn | CountVectorizer, fit_transform)11:20 - Building a text classification model using bag-of-words (SVM)14:07 - Predicting new utterances classes using our model (transform)16:02 - Unigram, bigram, ngrams (using consecutive words in your model)19:28 - Word vectors overview23:27 - Word vectors example code! (Using spaCy library)28:10 - Building a text classification model using word vectors34:04 - Predicting new utterances using our model~~ Miscellaneous NLP Techniques ~~40:42 - Regexes (pattern matching) in Python.52:30 - Stemming/Lemmatization in Python (text normalization w/ NLTK library)1:01:17 - Stopwords Removal (removing most common words from sentences)1:05:56 - Various other techniques (spell correction, sentiment analysis, part-of-speech tagging).~~ State-of-the-art Models ~~1:12:45 - Recurrent Neural Networks (RNNs) for text classification1:17:00 - Transformer architectures (attention is all you need)1:21:00 - Writing Python code to leverage transformers (BERT | spacy-transformers)1:25:00 - Writing a classification model using transformers/BERT1:29:37 - Fine-tuning transformer models1:31:16 - Bring it all together and build a high performance model to classify the categories of Amazon reviews!
Solving real-world data analysis problems with Python Pandas! (Lego dataset analysis) Keith Galli 2022-02-28 | In this video we walkthrough a data analysis project on DataCamp. This project has us walk through a Lego dataset and answer a few questions. To do our analysis we use the Pandas library of Python.Check out DataCamp!bit.ly/KeithGalliDCFeb22Link to my GitHub:github.com/KeithGalli/lego-analysisFrom the DataCamp website:The Rebrickable database includes data on every LEGO set that has ever been sold; the names of the sets, what bricks they contain, what color the bricks are, etc. It might be small bricks, but this is big data! In this project, you will get to explore the Rebrickable database and answer a series of questions related to the history of Lego!Link to Rebrickable database: rebrickable.com/downloadsSome skills worked on in this video:- Reading CSV files with Python- Filtering DataFrame based on conditional parameters- Grouping data by column values and aggregating itbtw, I apologize at about the 25-minute mark I started having microphone issues, I'll have it solved by my next video. Thank you to DataCamp for sponsoring this video :)-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------Song at the endgood morning by Amine Maxwell soundcloud.com/aminemaxwellCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoYMusic promoted by Audio Library youtu.be/SQWFdnbzlgI-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Video Timeline!0:00 - Introduction1:05 - Getting started w/ Lego analysis project2:33 - How to follow along if you are not a premium DataCamp subscriber (GitHub)4:01 - Project tasks overview5:40 - Basic exploration of the dataset9:45 - Task #1: What percentage of all licensed sets ever released were Star Wars Themed?24:23 - Task #2: In which year was Star Wars not the most popular licensed theme?34:00 - Bonus Task: How many unique sets were released each year (1955-2017)?42:26 - Conclusion!
The Future of Data Science! (Snickerdoodle AI/ML Framework) Keith Galli 2022-02-24 | Check out my website!keithgalli.comConsider supporting me on Patreon:patreon.com/keithgalliTutoring scholarship application form:https://forms.gle/5Br5ojgLTVttvME99Upgrade your coding environment with WayScript!wayscript.com/?via=keithIn this video I just wanted to provide some updates for the channel moving forward. Some of the main updates are that I will be adding channel memberships, offering consulting/tutoring, and working with more sponsors. Content will stay largely the same. A lot of long data science tutorials and project walkthroughs. I will also start posting more short videos on my second channel, TechTrek.Check out my second channel:youtube.com/c/TechTrekbyKeithGalliLet me know if you have any questions about anything discussed in this video.Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keith*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.----------------------------------------------------------------------Video timeline!0:00 - Intro2:34 - Channel Memberships - Join the Python Army!6:24 - Other ways to support the channel6:34 - Paid & Free Tutoring Opportunities8:21 - Sponsored Videos9:34 - Content on the Channel moving forward
How I became an unemployed MIT grad still living with my parents. Keith Galli 2022-02-18 | Happy to be back everyone! Here's a full update on what has been going on in my life and why I was absent from YouTube for so long.Raising awareness/money for men's mental health as part of this video! To learn more about the charity, Movember, check out their site: us.movember.com-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.-------------------------Song at the endgood morning by Amine Maxwell soundcloud.com/aminemaxwellCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/2vpruoYMusic promoted by Audio Library youtu.be/SQWFdnbzlgI
The data science machine!! (Lenovo ThinkPad P15 unboxing) Keith Galli 2021-03-23 | In this video, we unbox the Lenovo ThinkPad P15 Mobile Workstation. Thank you to Lenovo & NVIDIA for sending this my way. More data science projects on the way soon!!Tech specs:- Intel Core i9 processor- NVIDIA Quadro RTX 5000 GPU (16 gb)- 128 GB DDR4 ram (4 x 32 GB)- 2 TB SSD- 4K OLED Display (15")Hoping to make some videos on deep learning/computer vision projects with this machine. Also would like to compare a laptop like this to using a PC or using a machine in the cloud.Let me know if you have any questions about the machine or my experiences with it.Full tech spec range can be found here: lenovo.com/us/en/dc/undefined/laptops/thinkpad/thinkpad-p/ThinkPad-P15-Mobile-Workstation/p/22WSP15P5N1If you enjoyed this video, make sure to give it a LIKE and SUBSCRIBE if you haven't already :).-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
I wrote a program to automatically post a video if I hit 1000 subscribers! Keith Galli 2021-02-18 | In this video we use Python, the YouTube API, and AWS Lambda to create a program that can automatically publish (post) a video when a YouTube video hits 1000 subscribers. This was a fun project and I am excited to share it with all of you!Link to my second channel!youtube.com/channel/UCge9kgmp38FIjGK4xdJsThwProject source code: github.com/KeithGalli/auto-publish-youtube-video@coreyms's YouTube API Tutorials:Getting Started - youtu.be/th5_9woFJmkOAuth Access - youtu.be/vQQEaSnQ_bsYouTube API documentation: developers.google.com/youtube/v3/docsAWS boto3 library documentation: boto3.amazonaws.com/v1/documentation/api/latest/index.htmlLet me know if you have any questions! Please consider subscribing if you enjoyed this video :)-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Learn Python, Programming, & Data Science | Channel Trailer Keith Galli 2021-02-09 | A brief sample of what this channel is all about. My brother made this as an awesome birthday gift!😊Check out my brother's channel (he's currently working on some programming content): youtube.com/c/bobbyg603-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Solving Coding Interview Questions in Python on LeetCode (easy & medium problems) Keith Galli 2021-01-18 | In this video we walk through a series of eight coding interview questions on leetcode. These are algorithms problems that cover topics including data structures, time & space complexity, sorting, binary search, object oriented programming, breadth first search, tree traversals, and more. The difficulty of problems range from easy to medium and get gradually harder as you go through the video.Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalliIf you enjoy this video, make sure to SUBSCRIBE to not miss future videos.Checkout out my new channel where I will frequently be posting problems like these: youtube.com/channel/UCge9kgmp38FIjGK4xdJsThw/aboutFeel free to recommend the next problems that I should solve on YouTube!Links to problems:LeetCode 1678: leetcode.com/problems/goal-parser-interpretationLeetCode 1436: leetcode.com/problems/destination-cityLeetCode 1365: leetcode.com/problems/how-many-numbers-are-smaller-than-the-current-numberLeetCode 704: leetcode.com/problems/binary-searchLeetCode 409: leetcode.com/problems/longest-palindromeLeetCode 1561: leetcode.com/problems/maximum-number-of-coins-you-can-getLeetCode 1472: leetcode.com/problems/design-browser-historyLeetCode 1110: leetcode.com/problems/delete-nodes-and-return-forest-------------------------Timeline!0:00 - Introduction2:02 - LeetCode 1678 - Goal Parser Interpretation6:04 - LeetCode 1436 - Destination City14:07 - LeetCode 1365 - How Many Numbers Are Smaller Than the Current Number33:13 - LeetCode 704 - Binary Search46:13 - LeetCode 409 - Longest Palindrome55:01 - LeetCode 1561 - Maximum Number of Coins You Can Get1:10:09 - LeetCode 1472 - Design Browser History1:22:32 - LeetCode 1110 - Delete Nodes And Return Forest1:38:23 - Conclusion & Announcement!-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to Schedule & Automatically Run Python Code! Keith Galli 2020-11-27 | The first 1000 people to use the link will get a free trial of Skillshare Premium Membership: https://skl.sh/keithgalli11201In this video we learn various methods for how we can schedule and automatically run python code. We'll start by looking at cronjobs & windows task scheduler and then we will walk through running tasks in the cloud using AWS Lambda & Cloudwatch. The ultimate goal of this video is to learn how we can automatically generate & send an email with an analytics report every day. There are methods highlighted in this video that will work on Mac, Linux, & Windows.~ GitHub resources ~Link to source code: github.com/KeithGalli/scheduling-codeLink to analytics report code: github.com/KeithGalli/generate-analytics-report/tree/send_email~ Relevant videos ~How to generate an analytics report in python: youtu.be/UmN2_R4KEg8How to send an email with python (@coreyms ): youtu.be/JRCJ6RtE3xU~ Articles mentioned ~AWS Lambda memory limits: hackernoon.com/exploring-the-aws-lambda-deployment-limits-9a8384b0bec3Downloading python linux libraries on Windows: medium.com/@korniichuk/lambda-with-pandas-fd81aa2ff25e~ Other resources ~Python email library docs: docs.python.org/3.8/library/email.examples.htmlAWS Cron info (scheduling): docs.aws.amazon.com/AmazonCloudWatch/latest/events/ScheduledEvents.htmlLambda package setup: docs.aws.amazon.com/lambda/latest/dg/python-package.htmlThank you to Skillshare for sponsoring this video!-------------------------Video timeline!0:00 - What we’ll be doing in this video0:56 - Check out Skillshare! (sponsored)1:56 - How can we automate scripts? Overview of local, cloud, and serverless methods5:18 - Simple example of local script automation w/ cronjobs & windows task scheduler18:32 - How to schedule code on a cloud machine (use cronjobs)18:51 - Simple example of cloud script automation w/ AWS Lambda & Cloudwatch27:09 - Schedule & automate sending an email locally45:12 - Schedule & automate sending an email on the cloud w/ Lambda & Cloudwatch50:18 - Installing python packages in a serverless environment (zip uploads)55:50 - Generate & schedule sending analytics reports (locally)1:02:45 - Generate & schedule sending analytics reports in AWS Lambda1:07:03 - Limitations of lambda (max file upload size)1:09:00 - Generate & schedule sending analytics reports in AWS Lambda1:18:32 - Final thoughts & video recap!-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to Generate an Analytics Report (pdf) in Python! Keith Galli 2020-11-11 | The first 1000 people to use the link will get a free trial of Skillshare Premium Membership: https://skl.sh/keithgalli10201In this video we see how we can take visualizations that we plot in python libraries like Matplotlib & Plotly and package them into a nice looking analytics report using the fpdf library.Source code: github.com/keithgalli/generate-analytics-reportData: github.com/CSSEGISandData/COVID-19PyFPDF Docs: pyfpdf.readthedocs.io/en/latestHelpful blog post mentioned: towardsdatascience.com/covid-19-map-animation-with-python-in-5-minutes-2d6246c32e54Thank you to Skillshare for sponsoring this video!-------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli-------------------------Video Timeline!0:00 - What we will be doing in this video1:30 - Check out Skillshare! (sponsored)3:00 - Source code & Setup6:37 - Python FPDF library basics9:42 - Choosing our paper format (A4, Letter, etc)11:54 - Adding and resizing images in our PDF!18:52 - Helper method (which states & countries can we plot?)21:48 - Continuing to build out our report (exploring source code)27:17 - Adding additional pages to the report29:09 - Adding a title to our report32:37 - Adding a professional letterhead to report35:00 - Plotting geographic maps with covid-19 data (plotly)40:02 - Using datetime library to automatically grab & format yesterday’s date43:46 - Finalizing our report46:41 - Where are the colors set?48:11 - Final thoughts-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Solving real world data science tasks with Python Beautiful Soup! (movie dataset creation) Keith Galli 2020-10-01 | Data is everywhere! Enhance your career and acquire new skills by taking a course on DataCamp! Click here to take the first chapter of any course for FREE: bit.ly/36lKg44 (you’ll be supporting my channel too!)In this video we scrape Wikipedia pages to create a dataset on Disney movies. The video is formatted with tasks for you to try to solve on your own throughout. For the best learning experience, at each task you should pause the video, try the task on your own, and then resume when you want to see how I would solve it.We cover a wide range of Python & data science topics in this video. They include:- Web scraping with BeautifulSoup- Cleaning data- Testing code with Pytest- Pattern matching with regular expressions (Re library)- Working with dates (datetime library)- Saving & loading data with Pickle library- Accessing data from an API using Requests libraryLink to code & datasets: github.com/KeithGalli/disney-data-science-tasksPrevious tutorial on Beautiful Soup: youtu.be/GjKQ6V_ViQEIf you enjoyed this video, make sure to like & subscribe :)This video was sponsored by DataCamp---------------------Video timeline!0:00 - Video overview1:58 - Check out DataCamp! (sponsored)3:12 - SetupTask #1: Scrape the infobox from Toy Story 3 wiki page (save in python dictionary) (4:24)Link: en.wikipedia.org/wiki/Toy_Story_3Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) (28:52)Link: en.wikipedia.org/wiki/List_of_Walt_Disney_Pictures_films30:30 - Robots.txt (Are you allowed to scrape a site?)32:52 - Task #2: Scrape infobox for all movies in List of Disney Films (save as list of dictionaries) 57:27 - Save & Load dataset checkpoint (JSON file)Task #3: Clean our data! (1:02:04)1:09:28 - Task #3.1: Strip out all references ([1],[2],etc) from HTML1:16:39 - Task #3.2: Split up the long strings1:25:02 - Task #3.3: Examine errors we are getting1:30:27 - Task #3.4: Convert “Running time” field to an integer1:44:57 - Task #3.5: Convert “Budget” & “Box office” fields to floats2:33:53 - Task #3.6: Convert dates into datetime objects2:47:36 - Saving our data again (using Pickle)Task #4: Attach IMDB, Metascore, and Rotten Tomatoes scores to dataset (working with APIs) (2:53:18)Task #5: Save final dataset as a JSON file and as a CSV file (3:13:48)---------------------Extra resources!Setup Jupyter notebook: jupyter.readthedocs.io/en/latest/install/notebook-classic.htmlGoogle Colab (cloud-based notebook): colab.research.google.comLearn regular expressions: youtu.be/K8L6KVGG-7oPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliIf you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to Make a High Quality Tutorial Video! (workflow, camera equipment, and software tools) Keith Galli 2020-08-15 | In this video we look at how to make high quality instructional content on youtube and other platforms. We specifically look at a workflow to plan out, record, and then edit picture-in-picture tutorial videos. In addition, we overview some camera equipment that you can use as well as the screen recording & video editing software tools that I recommend.Thank you to Kylie Ying for providing some useful input for this video! :)Check out her channel: youtube.com/c/ycubedJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalliMy equipment:- Sony a6500 Camera: amzn.to/3ieDe42 - Sigma 16mm Lens: amzn.to/2PAEXEv - Rode Videomicro Microphone: amzn.to/31APkxJ- Andycine Camera Monitor: amzn.to/2DVxrSf - Snagit for screen recording- Adobe Premiere Pro for video editing- Adobe Photoshop for thumbnailsKylie’s equipment:- iPhone 6- Phone microphone (I think this one amzn.to/3aoCAOI)- Quicktime for screen recording- iMovie for editing- Photopea for thumbnailsVideo Editing Software:- Adobe Premiere Pro: adobe.com/products/premiere.html | what I use- Hitfilm Express (free): fxhome.com/hitfilm-express- Davinci Resolve (free): blackmagicdesign.com/products/davinciresolve- iMovieThumbnail/Banner Editing Software:- Adobe Photoshop: adobe.com/products/photoshop.html | what I use- Photopea (free, browser-based): photopea.comMicrophone options (USB connectivity):- Blue Snowball: amzn.to/2QhrH8h- Rode NT-USB (more expensive): amzn.to/3ha2b0fScreen Recording Software:- Snagit ($50 USD, Windows/Mac): techsmith.com/screen-capture.html | what I use- OBS: Open Broadcaster Studios (free, Windows/Mac/Linux): obsproject.com- Flashback Express (free, Windows): flashbackrecorder.com/express - Quicktime (free, Mac): blog.hubspot.com/marketing/how-to-record-your-screen- Xbox Windows 10 App (free, Windows): blog.hubspot.com/marketing/how-to-record-your-screenGeneral strategy to record video:1. Write out an outline you will follow for your tutorial2. Implement reference code for project3. Record tutorial in ~10 minute segments4. Sync video/screen recording, cut out pauses, fix volume, add effects5. Make professional thumbnail6. Give video descriptive title that includes keywords users are looking for7. Upload!---------------------Video timeline!0:00 - Intro & Video Overview0:49 - Start with an outline!1:20 - Planning your tutorial2:16 - Filming your tutorial (camera equipment, screen recording software)5:32 - Editing your tutorial (video editing software options, workflow)9:50 - Final touches to your video (thumbnail, description, general tips)13:10 - Conclusion & Channel Update---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Comprehensive Python Beautiful Soup Web Scraping Tutorial! (find/find_all, css select, scrape table) Keith Galli 2020-07-11 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video we walk through web scraping in Python using the beautiful soup library. We start with a brief introduction to HTML & CSS and discuss what web scraping is. Next we start getting into the basics of the beautiful soup library. This includes how to load a webpage, the basic commands you need to know such as find & find_all, grabbing strings from an HTML elements, etc. The final section of this tutorial is a series of exercises where you can practice your skills. In this section we scrape a webpage for links, we learn how to scrape a table and load it into a pandas dataframe, and we see how you can scrape & download a web image. Hope you enjoy!I’m looking into making future videos on more complex things you can do with web scraping as well as other libraries that are helpful such as Selenium & ScraPy. Subscribe to not miss those.Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------Resources used in this videoSimple webpage: keithgalli.github.io/web-scraping/example.htmlExample webpage: keithgalli.github.io/web-scraping/webpage.htmlLink to source code: github.com/KeithGalli/web-scrapingBeautiful Soup Documentation: crummy.com/software/BeautifulSoup/bs4/docCSS Selector Reference: w3schools.com/cssref/css_selectors.asp---------------------Learn more about HTML/CSS@Traversy Media HTML Crash Course: youtu.be/UB1O30fR-EE@Traversy Media CSS Crash Course: youtu.be/yfoY53QXEnI Codecademy: codecademy.com/catalog/language/html-css---------------------Video timeline!0:00 - Intro & Video Overview1:09 - What is web scraping?3:51 - Introduction to HTMLUsing the beautiful soup library (5:29)6:31 - Loading in a webpage (requests library)8:21 - Starting to scrape9:18 - find & find_all methods16:00 - Finding specific text/strings in our HTML (regex)18:38 - Select method (CSS path selections)25:55 - Grabbing the string/text from an HTML element28:17 - Getting a property of HTML element (href, src, id, class, etc)29:41 - Code navigation (parents, children, siblings)Let’s practice our skills! (33:57)35:53 - Exercise #1: Grab all social links on webpage in 3 different ways42:09 - Exercise #2: Scrape an HTML table into a Pandas Dataframe53:09 - Exercise #3: Grab all fun facts that contain the word “is”57:59 - Exercise #4: Use beautiful soup to help download an image from a webpage1:04:20 - Exercise #5: Solve the mystery challenge!!!---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Real-World Python Neural Nets Tutorial (Image Classification w/ CNN) | Tensorflow & Keras Keith Galli 2020-06-08 | In this video we walk through the process of training a convolutional neural net to classify images of rock, paper, & scissors. We do this using the Tensorflow & Keras libraries. This is a follow-up to the first video I posted on neural networks. Introduction to Neural Nets: youtu.be/aBIGJeHRZLQLink to my code (github): github.com/KeithGalli/neural-netsLink to Google Colab file: colab.research.google.com/drive/1MiRP2fwgGg6zfEnLuOZ_6X7lmeePTFaR?usp=sharing---------------------Resources!Learn more about CNNsGood written overview: towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53Good video overview (@CodeEmporium ): youtu.be/m8pOnJxOcqYIllustrated Examples of CNNs: towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614dMNIST Example: keras.io/examples/mnist_cnnLearn more about TensorFlow datasetstensorflow.org/datasets/overviewtensorflow.org/datasets/catalog/overviewLearn more about KerastunerDocumentation: keras-team.github.io/keras-tuner@sentdex : youtu.be/vvC15l4CY1Q@krishnaik06 : youtu.be/OzLAdpqm35EPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------Video Timeline!0:00 Video Overview0:33 Getting Started (Setup & Installation)2:24 Finding datasets to use6:02 Data Preparation10:26 Additional Data Prep (Convert data to NumPy format)15:22 Reshape Data & Normalize values between 0-119:39 Train our first network to classify images25:06 Convolutional Neural Net (CNN) approach28:48 Using GPU on Google Colab (speed up training)31:22 Improving our CNN (reduce image size, max pooling, dropout, etc)40:18 Using Kerastuner to automatically pick best hyperparameters52:50 Save & Load our models54:16 Plot NumPy arrays as images57:38 Convert JPG/PNG images to NumPy1:00:20 Final thoughts---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliIf you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Introduction to Neural Networks in Python (what you need to know) | Tensorflow/Keras Keith Galli 2020-05-10 | Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalliIn this video we start by walking through some of the basics. We look at why we use neural networks and how they function. We do an overview of network architecture (input layer, hidden layers, output layer). We talk a bit about how you choose how many hidden layers and neurons to have. We also look at hyperparameters like batch size, learning rate, optimizers (adam), activation functions (relu, sigmoid, softmax), and dropout. We finish the first section of the video talking a little about the differences between keras, tensorflow, & pytorch.Next, we jump into some coding examples to classify data with neural nets. In this section we load in data, do some processing, build our network, fit our data to it, and then finally evaluate our model. The examples get more complex as we go along. Some setup instructions for the coding portion of the video are found below.To install Tensorflow, download Anaconda: docs.anaconda.com/anaconda/installData & code used in tutorial: github.com/KeithGalli/neural-netsI’m going to post a follow up video to this soon where we walk through a real world example where we automatically classify images of hands for the game of rock, paper, scissors. Hopefully that should be up about 2 weeks from now. (EDIT: part 2 has been posted, link below)If you enjoyed this video, make sure to like & subscribe. Feel free to leave any questions in the comments section.Part 2! youtu.be/44U8jJxaNp8––––––––––––––––––––––––––––––Finally by Loxbeats soundcloud.com/loxbeatsCreative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download: http://bit.ly/FinallyLoxbeatsMusic promoted by Audio Library youtu.be/fGquX0Te1Yo––––––––––––––––––––––––––––––Video timeline!0:00 Video overview1:34 Why use neural networks3:08 How neural nets work (architecture basics)6:11 Hyperparameter overview (batch size, optimizer, dropout, learning rate, epochs)7:53 How do we choose layers, neurons, & other parameters?9:08 Why do we need an activation function?10:20 What activation function should I use?11:25 Keras vs Tensorflow vs PyTorch12:30 Coding starts (github & setup)14:07 Writing our first neural network (linear example)18:45 Selecting optimizer & loss function (model.compile)23:45 Fitting training data to our model (model.fit)27:31 Shuffle order of training data30:12 Evaluate model on test data (model.evaluate)32:00 Example #2: Classifying quadratic data36:06 Example #3: Classifying 6 clusters of data (try on your own)41:03 Using network to predict a single data point (model.predict)43:27 Example #4: Classifying multiple labels at a time (BinaryCrossentropy loss)55:19 Example #5: Classifying our complex data from start of video59:00 Conclusion & Next steps of learning neural nets---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIf you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs
Python Data Science Project Ideas! (for all skill levels) Keith Galli 2020-04-07 | In this video, we walk through eight project ideas to help you build up your data science skills. All of the resources mentioned in the video can be found here in the description. Covid-19 Analysis (2:16):- Johns Hopkins Data: github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series- Code that I wrote: github.com/KeithGalli/Data-Science-Project-Ideas/blob/master/Covid/Covid%20Analysis.ipynb- Kaggle: kaggle.com/covid19- @sentdex kaggle video: youtu.be/S6GVXk6kbcs- @3blue1brown simulations video: youtu.be/gxAaO2rsdIsBoard Game AI (3:16):- Overview video: youtu.be/y7AKtWGOPAE- Implementing minimax algorithm in python: youtu.be/MMLtza3CZFM- Reinforcement learning (Snake) tutorial: towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a- AlphaZero chess tutorial: towardsdatascience.com/create-ai-for-your-own-board-game-from-scratch-alpha-zero-part-3-f22761372245- @Google_DeepMind AlphaGo Documentary: youtu.be/WXuK6gekU1YReddit, Data is Beautiful (4:50):- Thread url: reddit.com/r/dataisbeautiful- Population changes chart: reddit.com/r/dataisbeautiful/comments/fr8q34/change_in_population_by_county_between_2010_and- S&P 500 recoveries chart: reddit.com/r/dataisbeautiful/comments/frzlbt/sp_500_recovers_during_major_crashes_ocText Sentiment Analysis Tool (6:48):- My full tutorial (machine learning w/ sklearn): youtu.be/M9Itm95JzL0- Learn about Transformers: http://jalammar.github.io/illustrated-transformer- BERT Paper: arxiv.org/pdf/1810.04805.pdf- Spacy NLP Library: explosion.ai/blog/spacy-transformers- YouTube API: developers.google.com/youtube/v3/quickstart/pythonSports Analysis (8:34):- My script to webscrape sports data: github.com/KeithGalli/Data-Science-Project-Ideas/blob/master/Sports/extract_data.py- Basketball reference site that I scraped: basketball-reference.com/leagues/NBA_2020_per_game.htmlStock Trading Bot (10:12):- Alpaca Site: https://alpaca.markets/- Alpaca Tutorials: https://alpaca.markets/docs/get-started-with-alpaca/tutorial-videos/- Quantopian (to learn more & backtest your trading strategies): quantopian.com/tutorials/getting-startedHouse Pricing Prediction (12:02):- Link to competition: kaggle.com/c/house-prices-advanced-regression-techniquesMiscellaneous Kaggle Projects (13:06):- Kaggle Data: kaggle.com/datasets- Airbnb Data: kaggle.com/dgomonov/new-york-city-airbnb-open-data- My real world data science tutorial: youtu.be/eMOA1pPVUc4Hope you guys enjoyed this video! If you have any questions about any of these projects let me know in the comments. Like & Subscribe if you haven't already :).Some skills you should hopefully take away from these projects:- Analysis with Python Pandas Library- Visualization with Python Matplotlib Library- AI/Machine Learning skills with scikit learn library- Regression Techniques- Exploratory Data Analysis ---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------------------------------~ Intro Music ~Track: Sunflower — Soyb [Audio Library Release]Music provided by Audio Library PlusWatch: youtu.be/dG1U3NuR9PkFree Download / Stream: alplus.io/sunflower---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsPractice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli
Professional Code Refactor! (Cleaning Python Code & Rewriting it to use Classes) Keith Galli 2020-03-22 | In this video we take the code from my “How to Program a Game (in Python)” tutorial and refactor it to be of a more professional quality. To do this we abstract out similar parts of the code into their own classes & files. Once the new code is written, we go through some examples of how we can extend its functionality in fun ways. We make a couple more difficult levels of the game which is made very easy by the new code architecture. The final product is a lot more similar to code you would write as a full time software developer at a company like Google, Facebook, etc…As always I left a video timeline in the comments! If you enjoy this video please consider giving it a like & subscribe for future content :). If you have any questions let me know in the comments!Follow the homies!instagram.com/shaneboyer_ instagram.com/_neos Source code for Game (original & refactored):github.com/KeithGalli/Basic-Python-Game Link to original How to Program a Game (in Python) Tutorial:youtu.be/-8n91btt5d8 Link to video on Classes & Object Oriented Programming:youtu.be/tmY6FEF8f1o Additional information on Getters & Setters:youtu.be/jCzT9XFZ5bw Some great resources if you want to write better/cleaner code! (affiliate links)python.org/dev/peps/pep-0008 amzn.to/2U90SWk Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli--------------------- Follow me on social media! Instagram | instagram.com/keithgalli Twitter | twitter.com/keithgalli ---------------------Video Timeline! (& some additional resources)0:00 Video overview & code setup3:04 Creating a Player Class10:43 Creating subclasses for our Enemy & HumanPlayer11:26 Creating a Color class14:35 Creating a screen class or all our game visuals27:00 Creating a game class for all our game logic32:54 Information on Getters & Setters in Python38:09 Bringing it all together in our main.py file!43:36 Testing our code & Debugging issues47:20 if _name_ == "__main__" information48:26 Creating more difficult game levels using our new class structure!54:00 Fixing the "cheat" where you can go off the screen55:44 Loading in custom images as the enemy blocks*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Everything you need to know about Classes in Python! (Object Oriented Programming Tutorial) Keith Galli 2020-02-29 | Follow me instagram.com/keithgalli for more tech content!Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalliDownload Kite: kite.comIn this video we overview the main things I think are pointing when you are starting to learn about classes & integrate them in your everyday code. In this video we cover:- why should we use object oriented programming (oop) ?- the basics of defining a class in python (__init__)- class methods- what the heck is "self"?- Inheritance & Subclassing- Operator overridingA timeline is in the comments.If there are topics you felt like I left out, let me know in the comments! I'll potentially make a follow up video to this one.For an example of me structuring my code with classes, check out this machine learning video:youtu.be/M9Itm95JzL0If you want to practice using classes, try rewriting the game we build in this video to use them:youtu.be/-8n91btt5d8^(I'll update the github repo for that video with my solution to this soon)Resources:Github code: github.com/KeithGalli/python-classes-tutorialOperator overloading: geeksforgeeks.org/operator-overloading-in-python---------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------Today’s merch!Creator: @ChilledCow Website: teespring.com/stores/chilledcow-shop---------------------Video timeline!0:00 - Introduction1:34 - Why use classes?2:27 - Defining our first class in Python6:05 - Class methods13:53 - Passing in default keyword arguments (size of our polygons)20:18 - Inheritance & subclassing (using the super() method)24:43 - Class method overriding26:06 - Plotting points with classes28:09 - Operator overloading---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs
Generating Mock Data with Python! (NumPy, Pandas, & Datetime Libraries) Keith Galli 2020-02-01 | In this video we write a python script to automatically generate a sales dataset. To do this we use the NumPy, Pandas, Calendar, & Datetime libraries. This is ultimately the data that we used in my last video “Solving real world data science problems with python pandas”.Link to the last video:youtu.be/vmEHCJofslg Link to finished code on GitHub: github.com/KeithGalli/Pandas-Data-Science-Tasks/tree/master/MiscUseful resources!NumPy Tutorial: youtu.be/GB9ByFAIAH4 Pandas Tutorial: youtu.be/vmEHCJofslg Datetime library documentation: docs.python.org/3/library/datetime.htmlDetailed video description!We start by creating a simple dataframe and programmatically adding rows of product purchases to it. We use the random library to select these products.We make our data more realistic by utilizing normal distributions and geometric distributions in numpy to spread out the number of purchases we make and the quantity of each item purchased.We use the datetime library to allow us to generate thousands of different times for each purchase with the most common times peaking around 12pm and 8pm.We take a list of the most common US street addresses to help us randomly generate addresses for each purchases.Hope you guys enjoy! Make sure to subscribe if you haven’t already :)Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------------------------------Today’s merch!Creator: @Chris Chann Website: unsatisfied.co---------------------------------------------Video Timeline!0:00 - Intro & Background Info1:15 - What we're creating in this video!2:03 - Start writing code (generating a simple dataframe & csv)8:26 - Task: Making our data more realistic, selecting some products with higher probability than others14:15 - Task: Generate 12 months worth of data in 12 csvs (calendar library, f-strings)18:12 - Make some months have more purchases than others19:28 - Normal distributions in NumPy23:43 - Improving speed of our code (making testing easier)26:41 - Task: Generate random addresses for our data35:03 - Task: Generate order times for purchases (datetime library overview)40:02 - Using timedelta objects to add & subtract time from dates45:09 - Generate a realistic quantity ordered for each product (using numpy geometric distribution)49:38 - Add multiple items being more likely to be sold together and cleaning code a bit*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Solving real world data science tasks with Python Pandas! Keith Galli 2020-01-14 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video we use Python Pandas & Python Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. Setup!Github source code & data: github.com/KeithGalli/Pandas-Data-Science-Tasks Installing Jupyter Notebook: jupyter.readthedocs.io/en/latest/install.html Installing Pandas library: pandas.pydata.org/pandas-docs/stable/install.html Check out the first video I did on Pandas:youtu.be/vmEHCJofslg Check out the videos I did on Matplotlib:youtu.be/DAQNHzOcO5Ayoutu.be/0P7QnIQDBJYDetailed video description! (timeline can be found in comments)We start by cleaning our data. Tasks during this section include:- Drop NaN values from DataFrame- Removing rows based on a condition- Change the type of columns (to_numeric, to_datetime, astype)Once we have cleaned up our data a bit, we move the data exploration section. In this section we explore 5 high level business questions related to our data:- What was the best month for sales? How much was earned that month?- What city sold the most product?- What time should we display advertisemens to maximize the likelihood of customer’s buying product?- What products are most often sold together?- What product sold the most? Why do you think it sold the most?To answer these questions we walk through many different pandas & matplotlib methods. They include:- Concatenating multiple csvs together to create a new DataFrame (pd.concat)- Adding columns- Parsing cells as strings to make new columns (.str)- Using the .apply() method- Using groupby to perform aggregate analysis- Plotting bar charts and lines graphs to visualize our results- Labeling our graphsIf you enjoy this video, make sure to leave it a like and subscribe to not miss any future similar tutorials :).Check out the new "solving real world data science tasks" video I posted!youtu.be/Ewgy-G9cmbg---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------------------------------Video Timeline!0:00 - Intro1:22 - Downloading the Data2:57 - Getting started with the code (Jupyter Notebook)Task #1: Merging 12 csvs into a single dataframe (3:35)4:25 - Read single CSV file5:44 - List all files in a directory 7:06 - Concatenating files11:00 - Reading in Updated dataframeTask #2: Add a Month column (12:48)14:12 - Parse string in Pandas cell (.str)Cleaning our data!17:31 - Drop NaN values from df21:25 - Remove rows based on conditionTask #3: Add a sales column (24:58)25:58 - Another way to convert a column to numeric (ints & floats)Question #1: What was the best month for sales? (29:20)30:35 - Visualizing our results with bar chart in matplotlibQuestion #2: What city sold the most product? (34:17)35:32 - Add a city column36:10 - Using the .apply() method (super useful!!)40:35 - Why do we use the lambda x ?40:57 - Dropping a column46:45 - Answering the question (using groupby)47:34 - Plotting our resultsQuestion #3: What time should we display advertisements to maximize the likelihood of purchases? (52:13)53:16 - Using to_datetime() method56:01 - Creating hour & minute columns58:17 - Matplotlib line graph to plot our results1:00:15 - Interpreting our resultsQuestion #4: What products are most often sold together? (1:02:17)1:03:31 - Finding duplicate values in our DataFrame1:05:43 - Use transform() method to join values from two rows into a single row1:08:00 - Dropping rows with duplicate values1:09:39 - Counting pairs of products (itertools, collections)Question #5: What product sold the most? Why do you think it did? (1:14:04)1:15:28 - Graphing data1:18:41 - Overlaying a second Y-axis on existing chart1:23:41 - Interpreting our results---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Programming More Trees in Python! (Recursion & Artwork) Keith Galli 2019-12-17 | In this video, we code up some more tree animations using python to support the #TeamTrees movement!Link to donate: teamtrees.org We start off showing how you can use recursion to make basic tree like structures. We then build on that and use more sophisticated recursive logic to make the trees look pretty realistic.Next we go through how you can draw paintings in Python. This method works by sampling points in a photograph and drawing brush strokes of the same color in the generated image. It ultimately outputs some pretty cool stuff :). Check out the first video I made for team trees! youtu.be/WG42fyCMsD0 Check out Kyle on Instagram for all the awesome nature pictures!instagram.com/k_rowejo Thanks for watching everyone! Thanks @Mark Rober & @MrBeast for all your efforts towards organizing this movement!! :)---------------------------Source code (recursive example): github.com/KeithGalli/TeamTrees I haven’t made the source code for the paintings public yet. Think I’ll post it here sometime in the next couple weeks. Feel free to bug me in the comments in case I forget.---------------------------Follow me on Twitter: twitter.com/keithgalliFollow me on Instagram: instagram.com/keithgalliJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli
Programming Trees in Python! (with the turtle graphics library) Keith Galli 2019-12-12 | Link to donate: teamtrees.orgSource code: github.com/KeithGalli/TeamTreesIn this video, we code up some cool tree animations using the python turtle library to support the #TeamTrees movement! :)We start off with some basics defining a few different functions to build up to a draw_tree function. Then we look at how you can clean up that base code using a Tree Class. Finally, we take our simple tree and replicate it many times to create some cool artwork.I'm going to post a part 2 in a couple days where I walk through using recursion to draw some more trees. Watch Part 2!!youtu.be/VUYPWxTe4NwPython Turtle Graphics Library Tutorial:youtu.be/pxKu2pQ7ILoTurtle graphics documentation:docs.python.org/3.3/library/turtle.html?highlight=turtle Thanks for watching everyone!Thanks @MarkRober & @MrBeast for all your efforts towards organizing this movement!! :)----------------------------While I was working on this video, I stumbled into some other Coding YouTubers who made cool videos for TeamTrees. If you're looking for similar content, check out! @The Coding Train : youtube.com/playlist?list=PLRqwX-V7Uu6bxNsa_3SfCPyF9Md9XvXhR @John Fish : youtu.be/SNdtMjkRVrU----------------------------Follow me on Twitter: twitter.com/keithgalliFollow me on Instagram: instagram.com/keithgalliJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli
How to Prepare for a Programming Interview! (Tips & Tricks) Keith Galli 2019-11-19 | In this video we walk through some of my tips for having success in a programming interview. We start with some high level details on how programming interviews work. Next we dive into my favorite places to find practice problems. After this we start talking about some specific tips you can do during an interview to improve your likelihood of receiving an offer. We end the video talking about what you should wear on interview day (men + women).I'll post some interview practice problem walkthroughs soon!If you have any questions, let me know in the comments!Also make sure to SUBSCRIBE if you haven't already :).Socials:Instagram: @KeithGalliTwitter: @KeithGalliThank you guys for watching, I appreciate all the support recently!!-------------------------------------------Links to sites mentionedCracking the Coding Interview Book: amzn.to/2GpToKjLeetcode: leetcode.com/problemset/allHackerRank: hackerrank.com/dashboardPlaces to read cool things:reddit.com/r/Pythonreddit.com/r/Programmingreddit.com/r/MachineLearningmedium.comnews.ycombinator.comJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc) Keith Galli 2019-09-30 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video we walk through a real world python machine learning project using the sci-kit learn library. In it we work our way to building a model that automatically classifies text as either having a positive or negative sentiment. We do this by using amazon reviews as our training data. Full video timeline in the comments!Link to Code & Data: github.com/keithgalli/sklearnRaw Data download:http://jmcauley.ucsd.edu/data/amazon/Sci-kit learn documentation:scikit-learn.org/stable/documentation.htmlMake sure you have sci-kit learn downloaded! To do this either run "pip install sklearn" or use python through Anaconda.Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------------Follow me on social media!Instagram: instagram.com/keithgalliTwitter: twitter.com/keithgalliTo get one of the cool shirts I was wearing:instagram.com/pagandvls---------------------------Video outline!0:00 - What we will be doing!3:40 - Sci-Kit Learn Overview6:38 - How do we find training data?9:33 - Download data11:45 - Load our data into Jupyter Notebook16:38 - Cleaning our code a bit (building data class)20:13 - Using Enums22:50 - Converting text to numerical vectors, bag of words (BOW) explanation25:45 - Training/Test Split (make sure to "pip install sklearn" !)33:45 - Bag of words in sklearn (CountVectorizer)40:05 - fit_transform, fit, transform methods42:05 - Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification47:50 - predict method53:35 - Analysis & Evaluation (using clf.score() method)56:58 - F1 score1:01:01 - Improving our model (evenly distributing positive & negative examples and loading in more data)1:20:36 - Let's see our model in action! (qualitative testing)1:22:24 - Tfidf Vectorizer1:25:40 - GridSearchCv to automatically find the best parameters1:31:30 - Further NLP improvement opportunities1:32:50 - Saving our model (Pickle) and reloading it later1:36:37 - Category Classifier1:39:14 - Confusion Matrix---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) Keith Galli 2019-07-10 | Check out stratascratch.com/?via=keith to practice your Python Pandas data science skills!This video overviews the NumPy library. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. A full video timeline can be found in the comments.Link to code used in video: github.com/KeithGalli/NumPyFeel free to watch at 1.5x to learn more quickly!If you enjoyed this video, please consider subscribing :). Let me know your feedback and what I should make a video on next. ----------------------------Videos of mine that use NumPy- Creating Connect 4 Game: youtu.be/UYgyRArKDEs- Plotting (with some use of NumPy): youtu.be/DAQNHzOcO5A- Generating Mock Data: youtu.be/VJBY2eVtf7o----------------------------Links with more information! NumPy vs Lists:jakevdp.github.io/blog/2014/05/09/why-python-is-slowIndexing: docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.htmlArray Creation Routines:docs.scipy.org/doc/numpy/reference/routines.array-creation.htmlMath Routines Docs:docs.scipy.org/doc/numpy/reference/routines.math.htmlLinear Algebra Docs:docs.scipy.org/doc/numpy/reference/routines.linalg.html----------------------------Video Timeline!0:00 - Introduction1:15 - What is NumPy1:35 - NumPy vs Lists (speed, functionality)9:17 - Applications of NumPy11:08 - The Basics (creating arrays, shape, size, data type)16:08 - Accessing/Changing Specific Elements, Rows, Columns, etc (slicing)23:14 - Initializing Different Arrays (1s, 0s, full, random, etc...)31:34 - Problem #1 (How do you initialize this array?)33:42 - Be careful when copying variables!35:45 - Basic Mathematics (arithmetic, trigonometry, etc.)38:20 - Linear Algebra42:19 - Statistics43:57 - Reorganizing Arrays (reshape, vstack, hstack)47:29 - Load data in from a file50:20 - Advanced Indexing and Boolean Masking55:59 - Problem #2 (How do you index these values?)---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Python Plotting Tutorial w/ Matplotlib & Pandas (Line Graph, Histogram, Pie Chart, Box & Whiskers) Keith Galli 2019-06-06 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithIn this video, we go through several real-world examples of using the Matplotlib & Pandas libraries to visualize data from CSV files. This is a follow-up to my introductory matplotlib video (youtu.be/DAQNHzOcO5A). Timeline for what we cover can be found in the comments. We start by creating line graphs of global gas price data over time. We review how to add a title, x & y axis labels, and scale our graph. We review how to customize the style and size of our charts. Next, we look at the FIFA 19 player data to create a histogram, a couple pie charts, and a box and whisker plot.If you enjoy this video, make sure to LIKE and SUBSCRIBE :) :)If you have any questions or feedback, please let me know in the comments!---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------------------------------Link to Source Code & Datasets!github.com/KeithGalli/matplotlib_tutorialMatplotlib Documentation:matplotlib.org/api/_as_gen/matplotlib.pyplot.htmlMatplotlib Fonts:http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samples Matplotlib Style Options:matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.htmlKaggle Data Link:kaggle.com/karangadiya/fifa19---------------------------------------------Video timeline!0:00 - Intro & Video Overview2:22 - Load Necessary Libraries & Download Data3:48 - Line Graph Example (Plotting Data from CSV file)21:52 - Histogram Example (FIFA Overall Skill Distribution)29:25 - Pie Chart #1 (Counting data in CSV) - Visualizing Soccer Foot Preferences36:41 - Pie Chart #2 (More advance Pandas Example) - Weight Distribution of FIFA Players47:49 - Box & Whisker Plot (Comparing FIFA teams to one another)1:00:37 - Final Comments---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Intro to Data Visualization in Python with Matplotlib! (line graph, bar chart, title, labels, size) Keith Galli 2019-06-01 | Practice your Python Pandas data science skills with problems on StrataScratch! stratascratch.com/?via=keithJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalliYay finally posting again. Hopefully this video will help you get comfortable working through the matplotlib library. I'm going to post a follow up video with real-world examples (combining pandas with matplotlib) and different types of plots next week. I originally was going to include that in this one, but I thought the video was getting a bit too long.In this video we walk through some of the basics of matplotlib. We start by making a simple line graph. We learn how to give the graph a title and label the x & y axis. We learn how to scale the graph by specifying the x & y tickmarks. After this, we restyle our line by passing in keyword arguments then do basically the same thing with a shorthand notation. We resize our graph and save it. Then we end with a simple bar chart. Source Code (includes code and data for next video as well):github.com/KeithGalli/matplotlib_tutorialMatplotlib Pyplot Documentation:matplotlib.org/api/_as_gen/matplotlib.pyplot.htmlFont List:http://jonathansoma.com/lede/data-studio/matplotlib/list-all-fonts-available-in-matplotlib-plus-samplesInstall libraries Needed for this video:Option 1:Open up a terminal window and typepip install matplotlibpip install numpypip install pandasOption 2:Download anaconda which will contain all the packages we need. A video on how to do this is here: youtu.be/YJC6ldI3hWkThanks for watching! Make sure to like and subscribe to not miss any future videos! Let me know if you have any questions.---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli--------------------------------------------Video Timeline:0:00 - Video overview (note real-world examples moved to next video)1:43 - Setup2:23 - Our first line graph!4:50 - Add title and labels for x & y axis7:13 - Change font type, size, etc.9:02 - Change tick marks (scale graph)11:20 - Add a legend12:25 - Restyle our line (color, line style, markers, width)15:53 - Shorthand notation to restyle lines17:27 - List of line customization options17:55 - Plot more complex lines22:20 - Resize Graph24:48 - Save Graph26:00 - Annotating/Cleaning Code27:00 - Bar Chart31:25 - Final Comments---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to Program a GUI Application (with Python Tkinter)! Keith Galli 2019-02-02 | Follow me twitter.com/keithgalli for more Python content!In this video, we learn how to build an application with a graphical user interface (GUI). This is a great video to learn how to use the Tkinter library as well as be introduced to some very important Python skills. We start off by introducing the fundamentals of the Tkinter library. We then create a frame for a weather application. We then connect it to a weather API and then finally convert the application to an executable file. Source code:github.com/KeithGalli/GUIFollow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalliTKinter Documentation:tutorialspoint.com/python/python_gui_programming.htmHope you enjoyed the video! Make sure to like and subscribe if you haven't already :)Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli----------------------Some potentially useful videos...API overview (not my video):youtube.com/watch?v=7YcW25PHnAAWorking with JSON in Python (not my video):youtube.com/watch?v=9N6a-VLBa2IInstalling Python: youtu.be/RJL6Y761TNEPython Tutorial #1: youtu.be/XM0CtrJYM2A----------------------Video timeline!0:00 - What we’re ultimately building2:05 - Getting started3:16 - The basics of every Tkinter App (Root)4:44 - Add a button to the screen6:20 - Passing in keyword arguments10:07 - Using frames to organize widgets11:53 - Setting custom colors14:00 - pack() to place widgets17:39 - grid() to place widgets19:53 - place() to place widgets25:49 - Setting the appearance of our final application33:45 - Adding background image36:00 - Implementing button/entry functionality39:49 - Connecting to the Weather API43:29 - Making a GET request51:36 - Formatting our response56:24 - Changing Font and Font Size59:58 - How to add weather icons to the app1:01:36 - Making our App an executable using PyInstaller---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to Program a Connect 4 AI (implementing the minimax algorithm) Keith Galli 2019-01-02 | In this video we take the connect 4 game that we built in the How to Program Connect 4 in Python series and add an expert level AI to it. We start out with a very simple implementation of just dropping a piece randomly and then progress to choosing a column based on score and then finally implementing the minimax algorithm with alpha beta pruning.Here is the link to the code we started with: github.com/KeithGalli/Connect4-PythonTo understand the theory behind this code, make sure to watch this video on how a board game AI works: youtu.be/y7AKtWGOPAETo understand alpha-beta pruning, check out this video: youtu.be/l-hh51ncgDITo build the game we started out with, watch these four videos:1. youtu.be/UYgyRArKDEs2. youtu.be/zD-Xuu_Jpe43. youtu.be/SDz3P_Ctm7U4. youtu.be/krCKxcrHeN4If you want to send me your finished AI, email me: kgmit18@gmail.comFollow me on social media too please :)instagram.com/keithgallitwitter.com/keithgalliThanks for watching! Hope you all learning somethingSUBSCRIBE to not miss out on any future videos---------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Programming Rock, Paper, Scissors with no If Statements (Python) Keith Galli 2018-11-23 | Taking the rock, paper, scissors game we programmed in the last video and this time trying to do it with no if statements. I liked the idea of making this video because being able to think about problems in multiple different ways is an important python skill. Let me know if you have any questions, and I hope you enjoy :).Make sure to SUBSCRIBE to not miss any future videos.Link to first rock, paper, scissors video:youtu.be/8TCtYbt7ARISource code:github.com/KeithGalli/rockpaperscissorsLink to setup SublimeREPL:packagecontrol.io/packages/SublimeREPL-------------------------PYTHON TUTORIAL SERIES:Why Learn Python? +Setup: youtu.be/RJL6Y761TNE 1. Math & Variables: youtu.be/XM0CtrJYM2A 2. Conditional Statements (if, elif, else): youtu.be/vsVGPcfxEiA 3. Functions: youtu.be/5U95tRdYySA 4. Lists & Tuples: youtu.be/_zFI6ytHHdY 5. For Loops & While Loops: youtu.be/WPF5M_Ic6Fc Learn to Program a game: youtu.be/-8n91btt5d8-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
How to program Rock, Paper, Scissors in Python! Keith Galli 2018-11-21 | Good exercise to get more comfortable with the python programming syntax. Let me know if you have any questions and I will post the follow up video (programming rock, paper, scissors with no if statements) in the next day or two. Hope you enjoy :)Make sure to SUBSCRIBE to not miss any future videos. Also please let me know what you would like to see me post in the future.Source Code:github.com/KeithGalli/rockpaperscissorsLink to setup SublimeREPL:packagecontrol.io/packages/SublimeREPL-------------------------PYTHON TUTORIAL SERIES:Why Learn Python? +Setup: youtu.be/RJL6Y761TNE 1. Math & Variables: youtu.be/XM0CtrJYM2A 2. Conditional Statements (if, elif, else): youtu.be/vsVGPcfxEiA 3. Functions: youtu.be/5U95tRdYySA 4. Lists & Tuples: youtu.be/_zFI6ytHHdY 5. For Loops & While Loops: youtu.be/WPF5M_Ic6Fc Learn to Program a game: youtu.be/-8n91btt5d8-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLsJoin the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby) Keith Galli 2018-10-26 | Practice your Python Pandas data science skills with problems on StrataScratch!stratascratch.com/?via=keithData & code used in this Tutorial: github.com/KeithGalli/pandasPython Pandas Documentation: http://pandas.pydata.org/pandas-docs/stableLet me know if you have any questions!In this video we walk through many of the fundamental concepts to use the Python Pandas Data Science Library. We start off by installing pandas and loading in an example csv. We then look at different ways to read the data. Read a column, rows, specific cell, etc. Also ways to read data based on conditioning. We then move into some more advanced ways to sort & filter data. We look at making conditional changes to our data. We also start doing aggregate stats using the groupby function. We finished the video talking about how you would work with a very large dataset (many gigabytes)I realized as I upload this video there are some additional things I want to talk about in a later video. The first thing that comes to mind immediately is using the apply() function on a dataframe to alter the data using a custom or lambda function. If you have questions on this or anything else before I get around to making a part 2, feel free to write me a note in the comments.If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos!Thanks for watching friends! Happy coding! :)Join the Python Army to get access to perks!YouTube - youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/joinPatreon - patreon.com/keithgalli---------------------------------------------Follow me on social media!Instagram | instagram.com/keithgalliTwitter | twitter.com/keithgalli---------------------------------------------Link to original source of data from Kaggle: kaggle.com/abcsds/pokemon---------------------------------------------Video Outline!0:00 - Why Pandas?1:46 - Installing Pandas2:03 - Getting the data used in this video3:50 - Loading the data into Pandas (CSVs, Excel, TXTs, etc.)8:49 - Reading Data (Getting Rows, Columns, Cells, Headers, etc.)13:10 - Iterate through each Row14:11 - Getting rows based on a specific condition15:47 - High Level description of your data (min, max, mean, std dev, etc.)16:24 - Sorting Values (Alphabetically, Numerically)18:19 - Making Changes to the DataFrame18:56 - Adding a column21:22 - Deleting a column22:14 - Summing Multiple Columns to Create new Column.24:14 - Rearranging columns28:06 - Saving our Data (CSV, Excel, TXT, etc.)31:47 - Filtering Data (based on multiple conditions)35:40 - Reset Index37:41 - Regex Filtering (filter based on textual patterns)43:08 - Conditional Changes47:57 - Aggregate Statistics using Groupby (Sum, Mean, Counting)54:53 - Working with large amounts of data (setting chunksize)-------------------------If you are curious to learn how I make my tutorials, check out this video: youtu.be/LEO4igyXbLs*I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.
