Statistics and Probability Full Course My CS 2023-06-09 | Statistics and probability are two closely related fields in mathematics that deal with the analysis, interpretation, and prediction of data and events. They provide tools and methods for making informed decisions and drawing meaningful conclusions in the face of uncertainty.Statistics: Statistics involves the collection, organization, analysis, interpretation, and presentation of data. It encompasses techniques for summarizing and describing data, making inferences about populations based on sample data, and testing hypotheses. Statistical methods are used in various fields, including social sciences, business, economics, medicine, engineering, and more.Key concepts in statistics include:Descriptive Statistics: These methods involve summarizing and describing data using measures such as mean, median, mode, standard deviation, variance, and graphical representations like histograms, box plots, and scatter plots.Inferential Statistics: Inferential statistics allows us to make predictions or draw conclusions about a population based on a sample. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.Probability Distributions: Probability distributions describe the likelihood of different outcomes in a random experiment or event. Common distributions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution.Sampling Techniques: Sampling involves selecting a subset of individuals or items from a population for data collection. Different sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, have specific advantages and applications.Statistical Tests: Statistical tests help determine the significance of observed differences or relationships in data. Examples include t-tests, chi-square tests, ANOVA (analysis of variance), and correlation analysis.Probability: Probability deals with the quantification of uncertainty and the likelihood of events occurring. It provides a framework for understanding random phenomena and making predictions based on underlying probabilities.Key concepts in probability include:Probability Basics: Probability is expressed as a number between 0 and 1, where 0 represents impossibility and 1 represents certainty. The probability of an event is determined by the ratio of favorable outcomes to all possible outcomes.Probability Rules: The rules of probability govern how events interact and are combined. These rules include the addition rule (for calculating the probability of the union of two events), the multiplication rule (for calculating the probability of the intersection of two events), and the complement rule (for calculating the probability of the complement of an event).Conditional Probability: Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), the probability of event A given event B.Probability Distributions: Probability distributions, such as the discrete probability distribution and continuous probability distribution, describe the probabilities of different outcomes or values in a random experiment. Distributions like the binomial distribution, Poisson distribution, and normal distribution are commonly used in probability theory.Random Variables: A random variable is a numerical quantity whose value is determined by the outcome of a random experiment. It can be discrete (e.g., number of heads in coin flips) or continuous (e.g., time taken to complete a task).Probability theory and statistics are closely connected, as probability concepts provide a foundation for statistical inference and analysis. They are fundamental tools for decision-making, risk assessment, experimental design, and modeling in a wide range of disciplines.
Unix Command Line Tutorial My CS 2023-01-09 | In this course you will learn about Unix command. This #unix course is designed comprehensively for beginners.
Cryptography and security full course My CS 2022-10-05 | Cryptography, or cryptology, is the practice and study of techniques for secure communication in the presence of adversarial behavior. More generally, cryptography is about constructing and analyzing protocols that prevent third parties or the public from reading private messages. Cryptography and security come hand in hand. If you want to dive into cyber security or computer #security having knowledge in #cryptography is needed.
Unix Tutorial for Beginners My CS 2022-08-23 | UNIX is an operating system which was first developed in the 1960s, and has been under constant development ever since. By operating system, we mean the suite of programs which make the computer work. It is a stable, multi-user, multi-tasking system for servers, desktops and laptops.UNIX systems also have a graphical user interface (GUI) similar to Microsoft Windows which provides an easy to use environment. However, knowledge of #UNIX is required for operations which aren't covered by a graphical program, or for when there is no windows interface available, for example, in a telnet session.The #shell acts as an interface between the user and the kernel. When a user logs in, the login program checks the username and password, and then starts another program called the shell. The shell is a command line interpreter (CLI). It interprets the commands the user types in and arranges for them to be carried out. The commands are themselves programs: when they terminate, the shell gives the user another prompt (% on our systems).The adept user can customise his/her own shell, and users can use different shells on the same machine. Staff and students in the school have the tcsh shell by default.The tcsh shell has certain features to help the user inputting commands.Filename Completion - By typing part of the name of a command, filename or directory and pressing the [Tab] key, the tcsh shell will complete the rest of the name automatically. If the shell finds more than one name beginning with those letters you have typed, it will beep, prompting you to type a few more letters before pressing the tab key again.History - The shell keeps a list of the commands you have typed in. If you need to repeat a command, use the cursor keys to scroll up and down the list or type history for a list of previous commands.
Linux System Administration Full Course My CS 2022-07-29 | This is a complete course on how to become a Linux System Administration. #Linux system administration is a process of setting up, configuring, and managing a computer system in a Linux environment. System administration involves creating a user account, taking reports, performing backup, updating configuration files, documentation, and performing recovery actions.
Multivariable Calculus full Course || Multivariate Calculus Mathematics My CS 2022-06-06 | Multivariable calculus (also known as multivariate calculus) is the extension of calculus in one variable to calculus with functions of several variables: the differentiation and integration of functions involving several variables, rather than just one. This course covers differential, integral and vector calculus for functions of more than one variable. This #multivariablecalculus will teach you almost all the topics that you need to know about.⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) Multivariable domains⌨️ (0:07:14) The distance formula⌨️ (0:12:15) Traces and level curves ⌨️ (0:18:16) Vector introduction⌨️ (0:23:22) Arithmetic operation of vectors ⌨️ (0:29:19) Magnitude of vectors ⌨️ (0:33:49) Dot product ⌨️ (0:38:49) Applications of dot products ⌨️ (0:45:41) Vector cross product ⌨️ (0:51:57) Properties of cross product ⌨️ (0:58:09) Lines in space ⌨️ (1:03:31) Planes in space ⌨️ (1:09:06) Vector values function ⌨️ (1:14:56) Derivatives of vector function⌨️ (1:21:07) Integrals and projectile Motion⌨️ (1:26:02) Arc length⌨️ (1:32:22) Curvature⌨️ (1:36:33) Limits and continuity⌨️ (1:42:10) Partial derivatives⌨️ (1:53:45) Tangent planes⌨️ (1:58:05) Differential⌨️ (2:01:58) The chain rule⌨️ (2:08:41) The directional derivative ⌨️ (2:13:14) The gradient ⌨️ (2:18:22) Derivative test⌨️ (2:23:00) Restricted domains⌨️ (2:28:06) Lagrange's theorem⌨️ (2:34:05) Double integrals⌨️ (2:41:21) Iterated integral⌨️ (2:51:13) Areas ⌨️ (2:56:54) Center of Mass⌨️ (3:01:49) Joint probability density⌨️ (3:06:33) Polar coordinates⌨️ (3:10:43) Parametric surface⌨️ (3:16:56) Triple integrals⌨️ (3:22:05) Cylindrical coordinates⌨️ (3:25:30) Spherical Coordinates⌨️ (3:29:57) Change of variables⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Statistics for Data Science My CS 2022-05-20 | Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. In this #statistics course you will about from the basic concepts such as defining statistics to the more advanced topic as hypothesis testing. This course will be beneficial for those who are looking forward to learn: statistics, statistics for #data_science, statistics for data analysis, statistics and probability etc.⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) What is statistics and related terms⌨️ (0:19:05) Sampling type⌨️ (0:39:10) Boxplot and histogram⌨️ (1:06:15) Measure of center spread⌨️ (1:43:20) Probability formulas⌨️ (2:09:25) Contingency table⌨️ (2:21:30) Bayes theorem⌨️ (2:35:35) Discrete probability distribution⌨️ (2:48:40) Binomial distribution⌨️ (3:08:44) Poisson distribution⌨️ (3:23:49) Uniform distribution⌨️ (3:46:53) Normal distribution ⌨️ (4:15:59) Central limit theorem⌨️ (4:31:00) Confidence interval⌨️ (4:53:00) Hypothesis testing⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Digital Forensic Crash Course for Beginners My CS 2022-03-21 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the first tutorial of digital forensics series which introduces digital #forensics in a comprehensive way.⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: cslesson.org
Business Mathematics My CS 2021-10-31 | Business mathematics are mathematics used by commercial enterprises to record and manage business operations. Commercial organizations use mathematics in accounting, inventory management, marketing, sales forecasting, and financial analysis.In this course following topics have been discussed in a very comprehensive way to understand business mathematics in depth.⭐️ Table of Contents⭐️ 📝0:00:00 Business math introduction📝0:26:28 Markups and markdown📝1:01:08 Discounts📝1:31:47 Currency conversion📝1:56:17 Costs and lines📝2:31:30 Breakeven📝2:50:36 Simple interest📝3:20:07 Compound interest📝3:57:32 Equivalent rate📝4:35:16 Payment plans📝4:58:57 Equations of value📝5:31:52 Annuities📝6:31:57 Back to back to annuities📝7:04:18 Bonds📝7:14:03 Perpetuities📝7:34:28 Mortgages⭐️ Credit ⭐️Course Author: Amy GoldlistWebsite: youtube.com/channel/UC6Dzvh0MxrUoqbeL6XfQD3gLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: cslesson.org
Deep Learning with TensorFlow My CS 2021-08-26 | Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. In this deep learning course you will learn about the fundamental of #deeplearning with TensorFlow. TensorFlow is a popular deep learning library implemented with #Python. This course is designed for beginners who want to understand about deep learning and it's implementation in real life. The session covers:0:00:00 Introduction0:01:38 What are Neural Networks?0:24:36 Training networks0:46:06 Classifying Irises1:27:58 Image analysis1:37:41 Convolutional Neural Networks1:47:43 Handwriting recognition2:11:45 Ethics of ML⭐️ Credit⭐️This course was taught by Dr Matt WilliamsYT channel: youtube.com/channel/UCv3dZ12v9kI4ZO1cAQouJvAUniversity of BristolLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Calculus 1 - full course for beginners My CS 2021-06-15 | Calculus, originally called infinitesimal calculus or "the calculus of infinitesimals", is the mathematical study of continuous change, in the same way that geometry is the study of shape and algebra is the study of generalizations of arithmetic operations.This is #calculus 1 course which will teach everything you need to know in order to start learning calculus. ⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) The Limit of a functtion⌨️ (0:17:33) Calculating limit using limit laws⌨️ (0:40:04) The precise definition of a limit⌨️ (0:50:19) Continuity⌨️ (1:19:41) Derivatives and rates of change⌨️ (1:54:20) The derivative as a function⌨️ (2:22:34) Differentiation formulas⌨️ (2:54:40) Derivative of trigonometric function⌨️ (3:08:58) The chain rule⌨️ (3:29:17) Implicit differentiation⌨️ (3:39:05) Related rates⌨️ (4:03:31) Linear approximation and differentials⌨️ (4:35:09) Maximum and minimum values⌨️ (5:00:17) The mean value theorem⌨️ (5:17:47) How derivatives affect the shape of a graph⌨️ (5:50:01) Limit of infinity horizontal asymptotes⌨️ (6:32:38) Optimization problems⌨️ (6:44:25) Newton's method⌨️ (6:58:52) Antiderivatives⌨️ (7:21:54) Areas and distances⌨️ (7:59:41) The definite integral⌨️ (8:42:59) Fundamental theorem of calculus⌨️ (9:06:58) Indefinite integrals and the net change theorem⌨️ (9:19:36) The substitution rule⌨️ (9:58:51) Areas between curves⌨️ (10:25:14) Volumes⭐️ Credit ⭐️Course Author by: Kamuela YongWebsite: youtube.com/channel/UCZ5JattaBe47AndeZxhAFjA/featured⭐️ "My CS" is non-profit and all the courses provided here are solely for educational purposes.⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: cslesson.org
Statistics and Probability Full Course || Statistics For Data Science My CS 2021-04-09 | Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data. In applying #statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. This course is the comprehensive explanation of all statistics which very crucial for data science as well. ⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) Introduction to statistics - basics terms⌨️ (1:17:05) Statistics - Location measures⌨️ (2:01:12) Statistics - Spread measures⌨️ (2:56:17) Statistics - Set theory⌨️ (4:06:11) Statistics - Probability basics⌨️ (5:46:50) Statistics - counting⌨️ (7:09:25) Statistics - Independence⌨️ (7:30:11) Statistics - Random variables⌨️ (7:53:25) Statistics - PMs and CDFs⌨️ (8:19:03) Statistics - Expectation⌨️ (9:11:44) Statistics - Binomial RVs⌨️ (10:02:28) Statistics - Poisson processes⌨️ (10:14:25) Statistics - Density function⌨️ (10:19:57) Statistics - Normal RVs ⭐ Credit ⭐Author: Curtis MillerLink: youtube.com/channel/UCUmC4ZXoRPmtOsZn2wOu9zg/featuredLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: https://cslesson.org
Digital Forensics Tutorial 5 || First step in Forensics Analysis My CS 2021-03-20 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the 6th tutorial of digital forensics series which introduces first step of digital #forensics in a comprehensive way.⭐ Referencesen.wikibooks.org/wiki/Introduction_to_Digital_Forensics⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: cslesson.org
Python Tutorial - Python for Beginners [Full Course] My CS 2021-02-28 | This course will provide you everything you need to know about Python programming from scratch. This #python #programming is designed for beginners. ⭐️ Table of Contents ⭐️ ⌨️ (0:00:00) Python programming - introduction⌨️ (1:28:58) Python programming - Conditionals⌨️ (2:08:25) Python programming - Functions⌨️ (2:31:29) Python programming - Functions exercise⌨️ (2:41:37) Python programming - Loops and iteration⌨️ (3:25:50) Python programming - Loops exercise⌨️ (3:34:29) Python programming - Strings⌨️ (4:03:56) Python programming - Strings exercise⌨️ (4:12:22) Python programming - Files⌨️ (4:34:02) Python programming - Files exercise⌨️ (4:43:46) Python programming - Lists⌨️ (5:01:23) Python programming - Lists exercise⌨️ (5:09:25) Python programming - Dictionaries⌨️ (5:38:59) Python programming - Dictionaries exercise ⌨️ (6:03:13) Python programming - Tuples⌨️ (6:25:24) Python programming - Tuples exercise⌨️ (6:35:30) Python programming - Regular expression⌨️ (7:03:35) Python programming - HTTP⌨️ (7:37:50) Python programming - HTTP example⌨️ (7:41:30) Python programming - Python objects⌨️ (8:03:33) Python programming - Database complex model⌨️ (8:12:17) Python programming - Database single table SQL⌨️ (8:22:23) Python programming - Databases⌨️ (9:04:13) Python programming - Foreign key⌨️ (9:15:43) Python programming - Databases join⌨️ (9:39:26) Python programming - Databases example⌨️ (10:01:05) Python programming - Visualization ⌨️ (10:47:50) Python programming - Visualization example🔗 Sample Code Zip: py4e.com/code3.zip🔗 Lecture Slides and Handouts: py4e.com/lectures3/🔗 Free Textbook: py4e.com/book.php🔗 Course Website: py4e.comThis course was created by Dr. Charles Severance (a.k.a. Dr. Chuck). He is a Clinical Professor at the University of Michigan School of Information, where he teaches various technology-oriented courses including programming, database design, and Web development."Python for Everybody" by Dr. Chuck Severance and the University of Michigan is licensed under CC BY.⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Digital Forensics Tutorial 5 || Digital Forensics Tools My CS 2021-02-24 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the 5th tutorial of digital forensics series which introduces digital #forensics tools in a comprehensive way.⭐ Referencesen.wikibooks.org/wiki/Introduction_to_Digital_Forensics⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: https://cslesson.org
Digital Forensics Tutorial 4 || Digital Forensics Terminologies My CS 2021-02-20 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the 4th tutorial of digital forensics series which introduces digital #forensics terminologies in a comprehensive way.Before we get too stuck into the nuts and bolts of digital forensic investigation lets take a moment to talk about the sort of terminology you are going to see throughout this book. As with any subject, digital forensics has its own arcane terms - and often redefines existing words to other meanings.⭐ Join our community ⭐Join our FB Group: facebook.com/groups/csles...Like our FB Page: facebook.com/cslesson/Website: https://cslesson.org
Digital Forensics Tutorial 3 || Digital Forensics process My CS 2021-02-20 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the third tutorial of digital forensics series which introduces digital #forensics process in a comprehensive way.⭐ Join our community ⭐Join our FB Group: facebook.com/groups/csles...Like our FB Page: facebook.com/cslesson/Website: https://cslesson.org
Digital Forensics Tutorial 2 || Types of Crime, Investigation and Analysis My CS 2021-02-19 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the part 2 of digital forensics where you will learn the following: Types of crimeTypes of investigation:Electronic discovery (eDiscovery):Intrusion investigation:Evidence and analysis:Here are some examples of the kind of analysis an examiner might be asked to undertake:AttributionAlibis and statementsIntentsEvaluation of sourceDocument authentication⭐ Join our community ⭐Join our FB Group: facebook.com/groups/csles...Like our FB Page: facebook.com/cslesson/Website: https://cslesson.org
Digital Forensics Tutorial 1 - An Introduction My CS 2021-02-18 | Digital forensics is, at root, a forensic science encompassing the recovery and investigation of material found in digital devices. This is the first tutorial of digital forensics series which introduces digital #forensics in a comprehensive way.⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslesson/Website: cslesson.org
Calculus Series and Sequences My CS 2021-01-12 | In this course we’ll be taking a look at #sequences and (infinite) #series. The following topics have been presented in this course in a very comprehensive way. ⭐ Table of Contents ⭐⌨️ (0:00:00) Sequences : intro⌨️ (0:10:56) Sequences example : listing terms⌨️ (0:19:53) Sequences: finding formula⌨️ (0:31:15) Limit⌨️ (0:56:07) Bounded sequences⌨️ (0:59:32) Convergent⌨️ (1:04:35) Monotone sequences⌨️ (1:33:54) Intro to series⌨️ (2:22:07) Integral test⌨️ (3:28:07) Ratio and root test⌨️ (3:54:38) Alternating series⌨️ (4:20:49) Derivative applications⭐ Credit ⭐This great course was developed by : Professor Sean FitzpatrickLicense: Creative Commons Attribution license (reuse allowed)YT: youtube.com/channel/UCNTQSJzbc90IjFJjlCIQpGQ⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Calculus 3 Full Course My CS 2020-11-30 | This course is about calculus 3 and the following topics have been presented in this course in very details.⭐ Table of Contents ⭐⌨️ (0:05) Sequences⌨️ (38:21) Infinite series⌨️ (1:07:31) The divergence and integral test⌨️ (1:24:07) Comparison test⌨️ (1:48:00) Alternating series⌨️ (2:09:10) Ratio and root tests⌨️ (2:31:10) Power series and function⌨️ (3:05:51) Properties of power series⌨️ (3:26:13) Taylor and maclaurin series⌨️ (3:50:07) Parametric equations⌨️ (4:01:31) Calculus of parametric curve⌨️ (4:29:03) Polar co-ordinates⌨️ (4:56:48) Area of polar co-ordinates⌨️ (5:33:18) Conic section⌨️ (6:16:07) Vectors in the plane⌨️ (6:48:37) Vectors in three dimensions⌨️ (7:01:42) The dot product⌨️ (7:17:00) The cross product⌨️ (7:33:27) Equations of lines and planes in space⌨️ (7:58:07) Equations of quadric surfaces⌨️ (8:24:17) Cylindrical and spherical co-ordinates⌨️ (8:55:59) Vector valued functions and space curves⌨️ (9:16:24) Calculus of vector-valued functions⌨️ (9:35:57) Length of curvature⌨️ (9:59:49) Motion in space⭐ Pre-requisite⭐⌨️ Precalculus: youtu.be/Tw0t2Y4tT-k⌨️ Calculus 1: youtu.be/8stueNPVl-I⌨️ Calculus 2: youtu.be/h4Vhh7aFmWw⭐ Credit ⭐This great course was developed by : Tyler WallaceLicense: Creative Commons Attribution license (reuse allowed)YT: youtube.com/user/wallacemath⭐ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Practical Statistics Full Course || Learn Statistics with Examples My CS 2020-11-15 | In this course you will learn about #statistics by solving statistical problem. Along the way you will solve statistics practice questions and learn their concepts.⭐ Table of Contents ⭐⌨️ (0:00) Statistics Example: Sample and Population⌨️ (1:07) Statistics Example: Representative sample⌨️ (3:10) Statistics Example: Representative sample 2⌨️ (4:40) Statistics Example: Sampling methods⌨️ (8:58) Statistics Example: Dot plot⌨️ (11:59) Statistics Example: Frequency table⌨️ (13:02) Statistics Example: Frequency distribution⌨️ (15:43) Statistics Example: Grouped frequency distribution⌨️ (18:57) Statistics Example: Histogram⌨️ (21:05) Statistics Example: Bar chart ⌨️ (23:26) Statistics Example: Stem and leaf plot⌨️ (26:02) Statistics Example: Scatter plot⌨️ (29:51) Statistics Example: Median⌨️ (31:55) Statistics Example: Weighted average⌨️ (35:03) Statistics Example: Mode⌨️ (35:45) Statistics Example: Range⌨️ (36:52) Statistics Example: Standard deviation⌨️ (39:33) Statistics Example: Regression line⌨️ (41:09) Statistics Example: Statistical prediction⌨️ (43:50) Statistics Example: Linear regression⌨️ (50:46) Statistics Example: The empirical rule⌨️ (51:39) Statistics Example: The empirical rule 2⌨️ (53:19) Statistics Example: The empirical rule 3⌨️ (57:19) Statistics Example: Z-scores⌨️ (58:50) Statistics Example: Z-scores 2⌨️ (1:00:45) Statistics Example: Margin of error⭐ Credit ⭐Course Author: Josiah HartleyLicense: Creative Commons CC-BY-SA licenseBook: http://hartleymath.com/versatilemath/read⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Information Security Tutorial My CS 2020-10-28 | This is a crash course on information security for beginners. It will provide you the basics knowledge and terminology about information security.⭐ Table of Contents ⭐⌨️ (0:00) Introduction to Security⌨️ (17:05) Threats, vulnerabilities and control⭐ Credit ⭐Course Author: Mike MurphyLicense: Creative Commons Attribution license (reuse allowed)YT: youtube.com/channel/UCndXB_kOP-C_KgZ2d_LNzbw⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Network Security Tutorial Course for Beginners My CS 2020-10-13 | This network course is for beginners who would like to know ins and out of network security. ⭐️ Table of Contents⭐️ 0:00 Network Security - Overview0:47 Ransomware and spyware09:50 Click fraud, spam13:40 DDoS attach16:54 Infections and social engineering23:12 Different group of attackers30:38 What is a botnet34:19 What botnet is used for41:01 Botnet architecture46:56 Fast flux50:54 Overview of detection method01:05:20 Machine learning based detection⭐️ Credit⭐️This course is offered by EPIC ErasmusCourse Link: https://www.moodle.aau.dk/course/index.php?categoryid=3006The production of this material is supported by the Erasmus+ programme of the European UnionLicense: Creative Commons Attribution license (reuse allowed)
SQLite crash course || SQLite Tutorial for Beginners My CS 2020-09-29 | SQLite is a C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. #SQLite is the most used database engine in the world. SQLite is built into all mobile phones and most computers and comes bundled inside countless other applications that people use every day. More Information...The SQLite file format is stable, cross-platform, and backwards compatible and the developers pledge to keep it that way through at least the year 2050. SQLite database files are commonly used as containers to transfer rich content between systems and as a long-term archival format for data. There are over 1 trillion (1e12) SQLite databases in active use.⭐ Credit ⭐Speaker: D. Richard HippLicense: Creative Commons Attribution license (reuse allowed)More Info: http://db.cs.cmu.edu/seminar2015/Carnegie Mellon UniversitySponsored by Yahoo! Labs⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Classical Mechanics Lecture Full Course || Mechanics Physics Course My CS 2020-09-21 | Classical #mechanics describes the motion of macroscopic objects, from projectiles to parts of machinery, and astronomical objects, such as spacecraft, planets, stars and galaxies.If the present state of an object is known it is possible to predict by the laws of #classical mechanics how it will move in the future (determinism) and how it has moved in the past (reversibility). In this mechanics #physics course you will learn about the following mechanics topics in details.⭐ Table of Contents ⭐⌨️ (0:00) Matter and Interactions⌨️ (28:25) Fundamental forces⌨️ (52:36) Contact forces, matter and interaction⌨️ (1:13:39) Rate of change of momentum⌨️ (1:36:43) The energy principle⌨️ (1:36:43) Internal Energy (matter and interaction)⌨️ (2:11:48) Quantization⌨️ (2:35:43) Multiparticle systems⌨️ (3:07:47) Collisions, matter and interaction⌨️ (3:19:23) Angular Momentum⌨️ (3:47:20) Entropy⭐ Credit ⭐Course Author: Prof. Steve SpicklemireLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Analysis of Variance (ANOVA) Overview in Statistics My CS 2020-08-19 | Analysis of variance (#ANOVA) is a collection of #statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. ANOVA was developed by the statistician Ronald Fisher⭐️ Table of Content ⭐️ 0:00 Introduction to ANOVA (analysis of variance)5:20 Notation of one way anova14:16 One way ANOVA calculation21:10 One way ANOVA table23:31 Notation of two way anova30:34 Calculation of two way anova36:01 Test statistics of two way anova47:18 Calculation of two way anova (with multiple reps)⭐️ Credit ⭐️Author: Professor KnudsonLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Computer Networking Complete Course - Basic to Advanced My CS 2020-07-13 | A #computer network is a group of computers that use a set of common communication protocols over digital interconnections for the purpose of sharing resources located on or provided by the #network nodes. The interconnections between nodes are formed from a broad spectrum of telecommunication network technologies, based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies.The nodes of a #computer_network may be classified by many means as personal computers, servers, networking hardware, or general purpose hosts. They are identified by hostnames and network addresses. Hostnames serve as memorable labels for the nodes, rarely changed after initial assignment. Network addresses serve for locating and identifying the nodes by communication protocols such as the Internet Protocol.In this comprehensive computer networking course you will learn ins and out of computer networking. You will learn from the very basic of computer networking to the very advance concept of networking. The following topic has been discussed in great details in this course:Introduction to Networking*** Topics ***Intro to Network Devices (part 1) (0:00)Intro to Network Devices (part 2) (7:14)Networking Services and Applications (part 1) (15:10)Networking Services and Applications (part 2) (22:55)DHCP in the Network (26:00)Introduction to the DNS Service (35:59)Introducing Network Address Translation (44:13)WAN Technologies (part 1) (51:16)WAN Technologies (part 2) (1:01:00)WAN Technologies (part 3) (1:8:10)WAN Technologies (part 4) (1:16:00)Network Cabling (part 1) (1:21:00)Network Cabling (part 2) (1:30:35)Network Cabling (part 3) (1:37:00)Network Topologies (1:42:00)Network Infrastructure Implementations (1:49:00)Introduction to IPv4 (part 1) (1:55:00)Introduction to IPv4 (part 2) (2:01:47)Introduction to IPv6 (2:15:00)Special IP Networking Concepts (2:25:36)Introduction to Routing Concepts (part 1) (2:33:39)Introduction to Routing Concepts (part 2) (2:39:00)Introduction to Routing Protocols (2:49:26)Basic Elements of Unified Communications (2:54:37)Virtualization Technologies (3:14:00)Storage Area Networks (3:05:34)Basic Cloud Concepts(3:11:10)Implementing a Basic Network (3:17:57)Analyzing Monitoring Reports (3:27:28)Network Monitoring (part 1) (3:33:10)Network Monitoring (part 2) (3:41:00)Supporting Configuration Management (part 1) (3:48:00)Supporting Configuration Management (part 2) (3:55:00)The Importance of Network Segmentation (4:01:12)Applying Patches and Updates (4:08:21)Configuring Switches (part 1) ( :14:10)Configuring Switches (part 2) (4:21:30)Wireless LAN Infrastructure (part 1) (4:30:00)Wireless LAN Infrastucture (part 2)Risk and Security Related ConceptsCommon Network VulnerabilitiesCommon Network Threats (part 1)Common Network Threats (part 2)Network Hardening Techniques (part 1)Network Hardening Techniques (part 2)Network Hardening Techniques (part 3)Physical Network Security ControlFirewall BasicsNetwork Access ControlBasic Forensic ConceptsNetwork Troubleshooting MethodologyTroubleshooting Connectivity with UtilitiesTroubleshooting Connectivity with HardwareTroubleshooting Wireless Networks (part 1)Troubleshooting Wireless Networks (part 2)Troubleshooting Copper Wire Networks (part 1)Troubleshooting Copper Wire Networks (part 2)Troubleshooting Fiber Cable NetworksNetwork Troubleshooting Common Network IssuesCommon Network Security IssuesCommon WAN Components and IssuesThe OSI Networking Reference ModelThe Transport Layer Plus ICMPBasic Network Concepts (part 1)Basic Network Concepts (part 2)Basic Network Concepts (part 3)Introduction to Wireless Network StandardsIntroduction to Wired Network StandardsSecurity Policies and other DocumentsIntroduction to Safety Practices (part 1)Introduction to Safety Practices (part 2)Rack and Power ManagementCable ManagementBasics of Change ManagementCommon Networking Protocols (part 1)Common Networking Protocols (part 2)*** Attribution ***Course Created by: PaceIT OnlineYouTube: https://www.youtube.com/channel/UCyTe...License: Creative Commons Attribution license (reuse allowed)*** Join our community ****Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
CompTIA A+ Full Video Course for Beginners My CS 2020-06-29 | In this course you will learn everything you need to know in order to sit for CompTIA A+ #certification exam. #CompTIA #A+ certified professionals are proven problem solvers. They support today’s core technologies from security to cloud to data management and more. CompTIA A+ is the industry standard for launching IT careers into today’s digital world.⭐️ Contents ⭐0:04 Operating System Features8:02 Microsoft OS and Requirements15:05 Install and Configure OS (part 1)19:55 Install and Configure OS (part 2)25:23 Intro to Command Line for Networking30:30 Command Line Tool for the OS (part 1)35:49 Command Line Tool for the OS (part 2)40:15 Administrative Tools & Features (part 1)46:18 Administrative Tools & Features (part 2)52:95 Administrative Tools & Features (part 3)58:35 Control Panels Utilities (part 1)1:05:36 Control Panels Utilities (part 2)1:10:09 Control Panels Utilities (part 3)1:14:14 Client Side Network Setup (part 1)1:20:14 Client Side Network Setup (part 2)1:27:34 Preventative Maintenance Best Practices1:32:13 Preventive Maintenance Tools1:37:46 Basic Operating System Security Settings (part 1)1:43:41 Basic Operating System Security Settings (part 2)1:54:19 The Basics of Client Side Virtualization1:59:32 Physical Security Measures2:05:09 IT (Digital) Security Measures2:10:58 Common Security Threats2:16:57 Securing the Workstation2:21:17 Data Disposal and Destruction Methods2:26:56 Secure SOHO Network2:33:12 Basics of Mobile Devices2:38:56 Basic Mobile Networking and Synchronization1:44:44 Secure Mobile Devices2:49:34 Troubleshooting Theory2:54:56 Troubleshooting Motherboards, RAM and CPUs3:01:41 Troubleshooting Hard Drives and RAID3:06:32 Troubleshooting Video and Displays3:12:22 Troubleshooting Networks Wired3:18:39 Troubleshooting Networks - Wireless3:24:45 Troubleshooting Windows OS (part 1)3:32:04 Troubleshooting Windows OS (part 2)3:38:20 Troubleshooting Security Threats3:44:19 Troubleshooting Laptops3:50:11 Troubleshooting Printers⭐️ Credit ⭐Course Created by: PaceIT OnlineLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
An Introduction to Reinforcement Learning My CS 2020-06-23 | Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.⭐️ Table of Content ⭐️ 0:00 Reinforcement learning: basic algorithm20:21 Reinforcement learning: Problem and varients⭐️ Credit ⭐️Prof. Laurenz WiskottInstitut für NeuroinformatikRuhr-Universität Bochum, Germany, EULicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Linux Tutorial For Beginners || Linux Command Line Tutorial My CS 2020-06-17 | The Linux command line is a text interface to your computer. Often referred to as the shell, terminal, console, prompt or various other names, it can give the appearance of being complex and confusing to use. Yet the ability to copy and paste commands from a website, combined with the power and flexibility the command line offers, means that using it may be essential when trying to follow instructions online, including many on this very website!This tutorial will teach you a little of the history of the command line, then walk you through some practical excercises to become familiar with a few basic commands and concepts.⭐️ Table of Content ⭐️ 0:00 Linux Command Line: Directory Operations (1 of 8)29:17 Linux Command Line: File Operations (2 of 8)37:31 Linux Command Line: File Operations (3 of 8)47:17 Linux Command Line: Finding Files (4 of 8)53:05 Linux Command Line: Redirection and Pipes (5 of 8)1:17:29 Linux Command Line: Processes (6 of 8)1:46:46 Linux Command Line: Users (7 of 8)2:04:22 Linux Command Line: File Permissions (8 of 8)⭐️ Credit ⭐️Author: Steven GordonWebsite: youtube.com/channel/UCu1lQtQ7SJU27bRlL6hzr9ALicense: Creative Commons Attribution license (reuse allowed)⭐️ Join Us ⭐️Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Algebra for Beginners | Algebra Full Course My CS 2020-05-26 | #Algebra is one of the broad parts of mathematics, together with number theory, geometry and analysis. In its most general form, algebra is the study of mathematical symbols and the rules for manipulating these symbols; it is a unifying thread of almost all of mathematics.⭐️ Contents ⭐(0:00) Exponent Rules(9:57) Simplifying using Exponent Rules (21:01) Simplifying Radicals(31:29) Simplifying Radicals, Snow Day Examples(41:30) Factoring(54:52) Factoring - Additional Examples(1:05:21) Rational Expressions(1:14:44) Solving Quadratic Equations (1:30:07) Rational Equations(1:40:29) Solving Radical Equations(1:52:00) Absolute Value Equations(1:59:28) Interval Notation(2:8:32) Absolute Value InequalitiesCompound Linear InequalitiesPolynomial and Rational Inequalities(2:18:53) Distance FormulaMidpoint Formula(2:26:03) Circles: Graphs and Equations(2:35:38) Lines: Graphs and Equations(2:44:08) Parallel and Perpendicular Lines(2:51:37) Functions(3:3:26) Toolkit Functions(3:10:32) Transformations of Functions(3:23:01) Introduction to Quadratic Functions(3:26:26) Graphing Quadratic Functions(3:35:35) Standard Form and Vertex Form for Quadratic Functions(3:39:51) Justification of the Vertex Formula(3:43:43) Polynomials(3:51:12) Exponential Functions(3:59:51) Exponential Function Applications(4:11:11) Exponential Functions Interpretations(4:20:49) Compound Interest(4:32:06) Logarithms: Introduction(4:40:47) Log Functions and Their Graphs(4:55:10) Composition of Functions(5:07:22) Inverse Functions⭐️ Credit⭐Author: Linda GreenWebsite: youtube.com/channel/UCkyLJh6hQS1TlhUZxOMjTFwLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Bayes Theorem and Naive Bayes Classifier My CS 2020-05-19 | In probability theory and statistics, Bayes' theorem (alternatively Bayes's theorem, Bayes's law or Bayes's rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes’s theorem allows the risk to an individual of a known age to be assessed more accurately than simply assuming that the individual is typical of the population as a whole.One of the many applications of Bayes’s theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in Bayes’ theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics. ( Read more: en.wikipedia.org/wiki/Bayes%27_theorem )** Topics of this course **(0:05) Conditional and Joint Probability (Bayes Theorem)(11:32) Naive Bayes Classifier** Credit ***Course Author: Building IntuitionWebsite: youtube.com/channel/UCjPrIyA7FzkAaK96xcKKILwLicense: Creative Commons Attribution license (reuse allowed)** Join our community **Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Python Tutorial for Beginners [Full Course] - Learn Python Full Course [2020] My CS 2020-05-12 | This course will teach you about Python programming from the very basic to the advanced. It also very comprehensive for absolute beginners who want to get their hands dirty with programming or simply want to understand and learn about #python #programming in 2020.⭐️ Contents ⭐⌨️ (0:05) Program structure⌨️ (4:49) Arithmetic operation⌨️ (11:05) Data types conversion ⌨️ (14:57) Data types⌨️ (21:02) Higher math functions⌨️ (25:28) Naming variables ⌨️ (30:43) The input #function ⌨️ (41:35) The print function ⌨️ (52:02) Variables ⌨️ (56:38) Floating point ⌨️ (58:47) Classes of errors ⌨️ (1:1:00) Types of errors ⌨️ (1:8:00) Nested loop ⌨️ (1:10:00) Turtle graphics ⌨️ (1:18:00) For loop ⌨️ (1:28:00) The range function ⌨️ (1:35:00) Using multiple turtles ⌨️ (1:43:00) The random modules ⌨️ (1:55:10) Functions ⌨️ (2:0:30) The local and global variables ⌨️ (1:10:08) Nested if ⌨️ (2:16:22) Selection statement⌨️ (2:22:08) Using elif for chain of condition⌨️ (2:28:10) The while loop⌨️ (2:38:10) The strings⌨️ (2:49:10) Strings - part 2⌨️ (2:57:10) Strings part3⌨️ (3:4:10) Lists⌨️ (3:14:10) Lists - part 2⌨️ (3:22:10) Nested lists⌨️ (3:29:10) Handling run time errors⌨️ (3:31:10) Files⌨️ (3:39:10) Files - part 2⌨️ (3:44:10) Dictionaries ⌨️ (3:56:10) Classes and object ⭐️ Credits⭐Course Author: Prof. J David EisenbergLicense: Creative Commons Attribution license (reuse allowed)⭐️ Join our community ⭐Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Cyber Security for beginners || Introduction to Cybersecurity My CS 2020-05-07 | In this course you will learn about cyber security, specifically on the definition of #cybersecurity, technology behind cyber security and #cyber #threats. This course will serve as an #fundamental foundation for cyber security enthusiasts. *** Topics of this course ***0:00 Cybersecurity definition9:26 Technology behind cyber security14:16 Cyber threats** Credit to Author **Produced by: How to engage in cyber policyScript Editors: Aditi Gupta, Jonathan Jacobs, Cathleen Berger, Lea KasparDirector/Producer: Meghna GuptaAnimation and Design: Gat PowellContributors: Nicolas Castellon, Mallory Knodel, Alex Cominos, Aditi Gupta, Jonathan Jacobs, Cathleen BergerProject Coordination: Daniela SchnidrigVoiceover Artist: Brigid Lohrey** Join our community **Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Probability statistics tutorial || Probability for data science My CS 2020-04-20 | In this course you will learn about probability #statistics for data science. This #probability course is very practical and comprehensive which will enable you to learn probability statistics easily and apply it to #datascience domain. ** Topics of this course **(0:5) Basics of Probability - events and outcomes(3:14) Basic Probabilities(5:23) Probability - Complements(7:03) Joint probabilities of independent events: P(A and B)(11:32) Probability of two events: P(A or B)(16:20) Probabilities from a table: AND and OR(19:15) Basic conditional probability(23:44) Conditional probability with cards(28:13) Conditional probability from a table(30:47) Probability of a diease given a positive test: Bayes Thorem ex1(34:49) Probability of a disease given a postiive test: Bayes Theorem ex2(39:32) Basic counting(42:52) Counting using the factorial(47:12) Permutations(51:19) Combinations(55:56) Combinations 2(58:14) Probabilities using combinations(1:3:0) Probabilities using combinations: cards(1:6:0) Probability: the birthday problem(1:10:54) Expected value(1:15:51) Expected value of insuranc** Credit **Video to accompany the open textbook Math in SocietyWebsite: http://www.opentextbookstore.com/mathinsocietyPart of the Washington Open Course Library License: creativecommons.org/licenses/by-sa/3.0/us** Join our community **Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
MySQL Tutorial for Beginners - Full Course My CS 2020-04-14 | #MySQL is an open-source relational database management system (RDBMS). Its name is a combination of "My", the name of co-founder Michael Widenius's daughter, and "SQL", the abbreviation for Structured Query Language. This MySQL course #tutorials is for #beginners. ** Topics of this course **MySQL for Beginners 001: Introducing MySQL (0:00)MySQL for Beginners 002: Installing MySQL and the Workbench(7:15)MySQL for Beginners 003: Using the Command Line (13:40)MySQL for Beginners 004: Creating Tables (20:11)MySQL for Beginners 005: Multiple Columns, int and Data Types (30:00)MySQL for Beginners 006: Null Values and Not Null (35:00)MySQL for Beginners 007: Mysql Storage Engines and Configuration(43:00)MySQL for Beginners 008: SQL Modes (58:00)MySQL for Beginners 009: Deleting All the Data in a Table (1:2:00)MySQL for Beginners 010: Primary Keys MySQL for Beginners 011: Auto Increment** Credit **Course Author: Cave of ProgrammingWebsite: youtube.com/channel/UCnAdXkr17iQS8YcYl0LhPdwLicense: Creative Commons Attribution license (reuse allowed)** Join our community **Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Artificial Intelligence Tutorial for Beginners My CS 2020-04-08 | In computer science, #artificial #intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".*** Credit ***Course created by the North Carolina School of Science and MathematicsWebsite: http://www.dlt.ncssm.eduLicense: creativecommons.org/licenses/by/3.0*** Join our community ***Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Mathematics for Machine Learning: Linear Algebra || Linear Algebra for Machine Learning My CS 2020-03-31 | In this course you will learn everything you need to know about linear algebra for #machine #learning. First part of this linear algebra course you will find the basics of #linear #algebra and second part of this course discussed about advanced linear algebra. This will allow to understand #machinelearning from #linearalgebra hence mathematical point of view. *** Topics Covered ***Vectors: Basic vectors notation, adding, scaling (0:00)Explaining the vector dot product (8:41)Introducing the vector cross product (15:58)More example of vector cross product (23:40)Thinking further about the cross product (30:15)Indroducing scaler triple product of vectors (38:10)Introduction to the matrix and matrix product (48:10)How to find determinant (58:00)Finding eigenvalues (1:8:0)Finding eigenvactors (1:17:00)Least square approximation: Introduction (1:36:00)Least square approximation: Fitting data to a straight curve(1:57:00)Least square approximation: the inverse of A transpose time A(2:38:11)Hamming Matrices (2:50:00)The functional calculus (3:27:00)Affine subspaces and transformations (4:15:00)Stochastic maps (05:02:00)*** Attribution ***Part 1(Basics): Simon BenjaminYT Channel: youtube.com/user/EvolutionOfScience/playlistsPart 2(Advanced): Arthur ParzygnatYT : youtube.com/channel/UCig5aK06RoHZomGrjhS_6ggLicense: Creative Commons Attribution license (reuse allowed)*** Join our community ***Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
CompTIA Security+ Full Course My CS 2020-03-23 | In this complete #compTIA #security+ course you will learn everything you need to know about IT / cyber security. Every topic of #IT #Security has been presented in a very comprehensive manner. Take this course to prepare yourself well to sit for the compTIA security+ exam.**** Topics Discussed **** Introduction to Network Devices (part 1) (0:00) Introduction to Network Devices (part 2) (8:06) Introduction to Network Devices (part 3) (15"50) Secure Network Administration Concepts (34:00) Cloud Concepts (41:00) Secure Network Design Elements and Components (48:00) Common Network Protocols (part 1) (55:20) Common Network Protocols (part 2) (1:01:00) Common Network Protocols (part 3) (1:08:00) Wireless Security Considerations (1:13:54) Risk Related Concepts (part 1) (1:23:12) Risk Related Concepts (part 2) (1:29:43) Risk Related Concepts (part 3) (1:36:08) Integrating Data and Systems w Third Parties(21:50) Risk Mitigation Strategies (1:41:27) Basic Forensic Procedures (1:46:17) Incident Response Concepts (1:54:15) Security Related Awareness and Training (1:59:38) Physical Security and Enviornmental Controls (2:08:03) Disaster Recovery Concepts (2:15:46) Risk Management Best Practices (2:23:30) Goals of Security Controls (2:30:54) Types of Malware (2:37:02) A Summary of Types of Attacks (part 1) (2:43:23) A Summary of Types of Attacks (part 2) (2:52:43) A Summary of Social Engineering Attacks (2:59:15) A Summary of Wireless Attacks (3:07:27) Types of Application Attacks part 1 Types of Application Attacks part 2 (3:10:51) Security Enhancement Techniques (3:15:33) Overview of Security Assessment Tools (3:22:21) Vulnerability Scanning vs Pen Testing (3:30:28) Application Security Controls and Techniques (3:37:10) Mobile Security Concepts and Technologies (part 1) (3:45:00) Mobile Security Concepts and Technologies (part 2)(3:52:00) Solutions Used to Establish Host Security (3:56:20) Controls to Ensure Data Security (4:04:00) Mitigating Risks In Alternative Environments (4:12:00) Summary of Authentication Services (4:21:20) Authentication and Authorization Basics (part 1) (4:28:00) Authentication and Authorization Basics (part 2) (4:35:00) Security Controls for Account Management Introduction to Cryptography (part 1) (4:44:00) Introduction to Cryptography (part 2) (4:52:00) Cryptographic Methods (part 1) (5:00:00) Cryptographic Methods (part 2)(5:05:44) Introduction to Public Key Infrastructure (part 1) (5:13:00) Introduction to Public Key Infrastructure (part 2) (5:19:00)*** Attribution ***Course Created by: PaceIT OnlineYouTube: youtube.com/channel/UCyTeMuUkzoB2Cl0AbRLtICgLicense: Creative Commons Attribution license (reuse allowed)*** Join our community ****Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
DoS attack | What is Denial of Service attack and How to initiate it My CS 2019-10-07 | In computing, a denial-of-service attack (#DoS attack) is a cyber-attack in which the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the Internet. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled.****************More Cyber security courses: youtube.com/watch?v=qvDg17PbSnU&list=PLmAuaUS7wSOMUeLR_y238QTaCDcYnKOO8******************== AttributionCourse Created by: Steven GordonVisit: youtube.com/user/StevesLectures/featuredLicense: Creative Commons Attribution license (reuse allowed)***********Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Linear Algebra for Beginners My CS 2019-09-14 | Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. Linear algebra is central to almost all areas of mathematics.Topic covered:Vectors: Basic vectors notation, adding, scaling (0:00)Explaining the vector dot product (8:41)Introducing the vector cross product (15:58)More example of vector cross product (23:40)Thinking further about the cross product (30:15)Indroducing scaler triple product of vectors (38:10)Introduction to the matrix and matrix product (48:10)How to find determinant (58:00)Finding eigenvalues (1:8:0)Finding eigenvactors (1:17:00)******************== AttributionCourse Created by: Simon BenjaminVisit: youtube.com/user/EvolutionOfScience/playlistsLicense: Creative Commons Attribution license (reuse allowed)***********Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Data Analysis for Beginners | Fundamentals of Data Analysis My CS 2019-09-01 | Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. In this course you will learn the basics of data analysis from scratch. The following topic has discussed in this course.- Data Analysis - Central Tendency - Mean, Median, and Mode (0:00)- Data Analysis - Central Tendency - Mean, Median, and Mode with TI (8:40)- Data Analysis - Quartiles, Interquartile Range, and Box-and-Whisker Plots (12:25)- Data Analysis - Quartiles, Interquartile Range, and Box-and-Whisker Plots with TI (21:58)- Data Analysis - Explanation of Percentile (25:34)- Data Analysis - Standard Deviation as a Way to Describe Data (28:49)- Data Analysis - Standard Deviation and Variance with TI-84+ (32:05)- Data Analysis - Frequency Tables and Histograms (35:39)- Sampling and Surveys - Sampling and Types of Studies (49:52)- Sampling and Surveys - Bias in Survey Questions (1:3:45)- Data Analysis - Normal Distribution (1:08)******************== AttributionCourse Created by: LameEStorageVisit: youtube.com/user/LameEStorageLicense: Creative Commons Attribution license (reuse allowed)***********Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Quantum Computing for Beginners | How to get started with Quantum Computing My CS 2019-08-21 | Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically.The field of quantum computing is actually a sub-field of quantum information science, which includes quantum cryptography and quantum communication.In this course you will get general overview of quantum computing which is suitable for beginners.If you like this course share it with your friends. Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Python Programming Full Course for Beginners | Basic to Advance My CS 2019-08-17 | Python programming is the most popular language. In the field of data science and machine learning #pythonprogramming is must needed skill. In this course you will learn about python programming from basic towards the advance. The following topics of Python programming have been explained very comprehensive way. Topics: Python Programming: Types, Variable, Mutability, object, state (0:00)Python Programming: Text, String, Escaping, Raw, Repr, Encoding and Methods (10:47:15)Python Programming: List, Range, Metrics (32:22)Python Programming: Tuples, Set (52:39)Python Programming: Arithmetic comparisons, Assignment, Identity, Equality and Membership (1:3:40)Python Programming: if, else, elif, decision control (1:20:41)Python Programming: Loops, for loops, while loops (1:30:20)Python Programming: Dictionary (1:47:11)Python Programming: scope, namespace, private (1:59:11)Python Programming: Functions, arguments and parameters (2:14:48)Python Programming: Decorators, nesting and first class function (02:39:48)Python Programming: comprehensions of generators, loops, dict (03:01:25)Python Programming: Recursion, iterable, generators, yield (03:20:30)Python Programming: Debugging, Exception Handling (03:36:22)*********Course content created by: Mnemonic AcademyLicense: Creative Commons Attribution license (reuse allowed)Visit Mnemonic Academy YouTube Channel and learn more: youtube.com/channel/UCrZjSLS1o3FJDS5hFoTCIPQNotebook Repository: github.com/dylanjorgensen/marshmallow*********If you like this course share it with your friends. Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
SAS tutorials for beginners | SAS tutorial for data analysis My CS 2019-08-06 | SAS is the leader in analytics. Through innovative analytics, BI and data management software and services, #SAS helps turn your data into better decisions. In this course you will learn how to do basic analysis of data using SAS. This SAS course is for beginners who want to explore. Following topics has been discussed in this SAS course. SAS 0: Importing Data SAS 1a: Sample Statistics SAS 1b: Simple Graphics SAS 02: One-Population Procedures SAS-03: Two-Population Procedures SAS-04: Multiple Comparisons Procedures SAS-05: Correlation and Regression SAS-06: Categorical Data Analysis *******Course developed by: F-Plus EducationVisit F-plus Education YouTube channel: youtube.com/watch?v=PPcW0RG2C4o&list=PL2fQFtLP7fTttoXrAy6ixLazSSug4I3rU*********Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonVisit Website : cslesson.org******************Like and Share this video and help others. Thanks
Quantum Physics Full Course | Quantum Mechanics Course | Part 2 My CS 2019-07-28 | Quantum mechanics (QM; also known as #quantum #physics, quantum theory, the wave mechanical model, or #matrixmechanics), including quantum field theory, is a fundamental theory in physics which describes nature at the smallest scales of energy levels of atoms and subatomic particles.In this course you will get exposed to the following topics of Quantum Mechanics in details:The Dirac delta function Bound states, scattering states, and tunneling Boundary conditions in the time independent Schrodinger equation The bound state solution to the delta function potential TISE Scattering state solutions to the delta function potential TISE Finite square well bound states Finite square well scattering states Linear algebra introduction Linear transformations Eigenvectors and eigenvalues Mathematical formalism in quantum mechanics Hermitian operator eigen-stuff Statistics in formalized quantum mechanics Generalized uncertainty principle Energy-time uncertainty Dirac notation Schrodinger equation in 3d From the TISE in 3d to spherical harmonics TISE in 3d radial behavior Hydrogen atom radial wavefunctions Hydrogen atom wavefunctions Hydrogen spectrum Angular momentum operator algebra Angular momentum eigenfunctions Spin in quantum mechanics Spin 1/2 Spin 1/2 in a B-field Angular momentum addition Two particle systems Exchange forces Free electrons in conductors Band structure of energy levels in solids Quantum statistical mechanics******************Author: Brant CarlsonYouTube link: youtube.com/channel/UCNIEAv633WRg4ubBIhOcwMg******************Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
R programming for Beginners | R programming for data Science My CS 2019-06-26 | R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. This video is about #R #programming which will help you understand about R and how to use R for #datascience tasks. This R course is very comprehensive, you don't need any prior knowledge of programming to start this course. The following R programming topics has covered in this course:Table of Contents: R 1.1 - Initial Setup and Navigation R 1.2 - Calculations and Variables R 1.3 - Create and Work With Vectors R 1.4 - Character and Boolean Vectors R 1.5 - Vector Arithmetic R 1.6 - Building and Subsetting Matrices R 1.7 - Section 1 Review and Help Files R 2.1 - Loading Data and Working With Data Frames R 2.2 - Loading Data, Object Summaries, and Dates R 2.3 - if() Statements, Logical Operators, and the which() Function R 2.4 - for() Loops and Handling Missing Observations R 2.5 - Lists R 3.1 - Managing the Workspace and Variable Casting R 3.2 - The apply() Family of Functions R 3.3 - Access or Create Columns in Data Frames, or Simplify a Data Frame using aggregate() R 4.1 - Basic Structure of a Function R 4.2 - Returning a List and Providing Default Argument R 4.3 - Add a Warning or Stop the Function Execution R 4.4 - Passing Additional Arguments Using an Ellipsis R 4.5 - Make a Returned Result Invisible and Build Recursive Functions R 4.6 - Custom Functions With apply()**********Course developed by: Google DevelopersYouTube channel: youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw**********If it's helpful for you, please share and help others too.Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Learn Machine Learning: FULL Course for Beginners [Without Math] My CS 2019-06-04 | Have you ever wondered what are those Data Science and Machine Learning that everyone is talking about, but you were too afraid to ask? By the end of this course, you’ll be able to learn what Data Science and Machine Learning precisely are and how they work.▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬Contents⌨️ Welcome to the Course⌨️ Introduction part to Data science and machine learning⌨️ Preliminary to understand Data Science and Machine learning⌨️ Machine learning Models⌨️ Evaluate Model Performance ⌨️ Best practices in data science and machine learning⌨️ Conclusion ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬This Course is developed by AI SCIENCES ACADEMYAI SCIENCES provides free courses and tutorials in Data Science, Machine Learning and AI for beginners like you!Follow AI Sciences!AI Sciences's Youtube Channel 👉 youtube.com/channel/UC8kFF39hsRrFfHM6-7A6APQAI Sciences's Website 👉 aisciences.netAI Sciences's Facebook Page 👉 facebook.com/aisciencesllcAI Sciences's LinkedIn Page 👉 linkedin.com/company/ai-sciencesAI Sciences's FREE eBooks 👉 aisciences.net/product-category/free-ebooks▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬👋 About AI Sciences: AI Sciences is e-learning company; the company publish online courses and books about data science and computer technology for anyone, anywhere. We are a group of experts, PhD students, and young practitioners of artificial intelligence, computer science, machine learning, and statistics. Some of us work for big-name companies like Google, Facebook, Microsoft, KPMG, BCG, and Mazars. We decided to produce courses and books mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory or lengthy reading. Today, we also publish more complete books on selected topics for a wider audience.Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.org
Data Science Full Course for Beginner | Data Science Tutorial My CS 2019-05-23 | In this complete data science course you will learn each and everything you need to know in order to be a data scientist. This #datascience course is beginner friendly. You will know about what data science is and how to do data science and what is required to do data science practically. You will learn about Data Science, Data Sourcing, Basic coding, mathematics and statistics as well.**************************************Explore Datalab.cc more. You will surely love thatYouTube Channel: youtube.com/user/datalabcc/playlistsWebsite: datalab.cc**************************************Join our community and stay up to date with computer science********************Join our FB Group: facebook.com/groups/cslessonLike our FB Page: facebook.com/cslessonWebsite: cslesson.orgTable of Contents:Part 1: Data Science: An Introduction: Foundations of Data Science Welcome - Data Science: An Introduction - 1.1 (0:00) Demand for Data Science - Data Science: An Introduction - 2.1 (2:00) The Data Science Venn Diagram - Data Science: An Introduction - 2.2 (7:30) The Data Science Pathway - Data Science: An Introduction - 2.3 (14:30) Roles in Data Science - Data Science: An Introduction - 2.4 (19:00) Teams in Data Science - Data Science: An Introduction - 2.5 (23:00) Big Data - Data Science: An Introduction - 3.1 (26:30) Coding - Data Science: An Introduction - 3.2 (31:00) Statistics - Data Science: An Introduction - 3.3 (34:00) Business Intelligence - Data Science: An Introduction - 3.4 (38:00) Do No Harm - Data Science: An Introduction - 4.1 (41:00) Methods Overview - Data Science: An Introduction - 5.1 (47:00) Sourcing Overview - Data Science: An Introduction - 5.2 (49:00) Coding Overview - Data Science: An Introduction - 5.3 (53:00) Math Overview - Data Science: An Introduction - 5.4 Statistics Overview - Data Science: An Introduction - 5.5 (56:00) Machine Learning Overview - Data Science: An Introduction - 5.6 Interpretability - Data Science: An Introduction - 6.1 (1:4:00) Actionable Insights - Data Science: An Introduction - 6.2 Presentation Graphics - Data Science: An Introduction - 6.3 Reproducible Research - Data Science: An Introduction - 6.4 Next Steps - Data Science: An Introduction - 7.1 (1:36:00) Part 2: Data Sourcing: Foundations of Data Science ( 1:44:00) Welcome - Data Sourcing - 1.1 Metrics - Data Sourcing - 2.1 Accuracy - Data Sourcing - 2.2 Social Context of Measurement - Data Sourcing - 2.3 Existing Data - Data Sourcing - 3.1 APIs - Data Sourcing - 3.2 Scraping - Data Sourcing - 3.3 New Data - Data Sourcing - 4.1 Interviews - Data Sourcing - 4.2 Surveys - Data Sourcing - 4.3 Card Sorting - Data Sourcing - 4.4 Lab Experiments - Data Sourcing - 4.5 A/B Testing - Data Sourcing - 4.6 Next Steps - Data Sourcing - 5.1Part 3: Coding ( 2:36:00) Welcome - Coding - 1.1 Spreadsheets - Coding - 2.1 Tableau Public - Coding - 2.2 SPSS - Coding - 2.3 JASP - Coding - 2.4 Other Software - Coding - 2.5 HTML - Coding - 3.1 XML - Coding - 3.2 JSON - Coding - 3.3 R - Coding - 4.1 Python - Coding - 4.2 SQL - Coding - 4.3 C, C++, & Java - Coding - 4.4 Bash - Coding - 4.5 Regex - Coding - 5.1 Next Steps - Coding - 6.1 Part 4: Mathematics (4:05:00) Welcome - Mathematics - 1.1 Elementary Algebra - Mathematics - 2.1 Linear Algebra - Mathematics - 2.2 Systems of Linear Equations - Mathematics - 2.3 Calculus - Mathematics - 2.4 Calculus & Optimization - Mathematics - 2.5 Big O - Mathematics - 3.1 Probability - Mathematics - 3.2 Bayes' Theorem - Mathematics - 3.3 Next Steps - Mathematics - 4.1 Part 5: Statistics (5:00:00) Welcome - Statistics - 1.1 Exploration Overview - Statistics - 2.1 Exploratory Graphics - Statistics - 2.2 Exploratory Statistics - Statistics - 2.3 Descriptive Statistics - Statistics - 2.4 Inferential Statistics - Statistics - 3.1 Hypothesis Testing - Statistics - 3.2 Estimation - Statistics - 3.3 Estimators - Statistics - 4.1 Measures of Fit - Statistics - 4.2 Feature Selection - Statistics - 4.3 Problems in Modeling - Statistics - 4.4 Model Validation - Statistics - 4.5 DIY - Statistics - 4.6 Next Step - Statistics - 5.1
