The 3 Types of Machine Learning Algorithms @insightsdev310

Insights Dev Today we are going to talk about the 3 types of ML algorithms

how they work,what are their usage in the real world and what issues we face while using them.
see more at my blog:
insightsdev.com
What is ML the very short version

In short ML is the ability to take data and use it to create a modal we can use to infer the structure or properties of new records

the current buzz environment is looking at ML as data in magic out machine able to do anything

This create a lot of cases of unrealistic expectations and misuse in implementation.

By understanding the premise of each type of ML, when to use it and its limitations we can start a meaningful conversation on how to implement this abilities in real applications

So this is why it's important to distinguish between the type of ML algorithms

Because each type is helpful for certain kind of problems and use different kind of data and vary in the insight you can get from it

the 3 types of ML algorithms are

Supervised learning - When we know what we are trying to infer and we have data of the result

Unsupervised learning -When we don't know what we are trying to infer or we know but we don't have enough data about the expected result

Reinforcement Learning - When the environment change by the action of the algorithm

So Lets start looking at each one in details

Supervised learning

this is the usually known as prediction algorithms it will receive as an input data that for each record we know what is the expected results for that data

a basic example for this is knowing :

red 2016 Mazda cost 30K$

red 2017 Mazda cost 35K$

we predict a Red 2018 Mazda will cost 40K$

but supervised learning ist limited to prediction of a value

you can use it to create:

Using customer service Q&A to give the most proper response for chatbot

automatically Tag Social Media using what user tagged in the past

emotion recognition in form an image using what user said they felt at that moment

The Limit of this approach is that each record need to be labeled with the expected result

this is a problem when :

Not enough records or no history

we don't know result for each record

we don't know what are we looking or what we looking for is vary rear

Unsupervised learning

To cope with the cases we don't know what is the meaning of the data or its not labeled.

We can use unsupervised learning to understand the inner relation between the property of the data and find pattern in the data and anomalies that divert from this patterns

for example by looking on the transactions in a supermarket we can discover:

that people how buy diapers also by beers

understand what characterize the customers hoe buy beer

find anomalies in purchase that can lead to detecting fraud

this can give you insight of to the patterns an anomaly but it lacks in a few area

there is no indication if a pattern or anomaly is good or bad

if something is an anomaly it doesn't mean its fraud

Lots of false detection of anomaly and pattern

the result can vary a lot by changing the model definition( like group to 5 groups instead of 10)

Reinforcement learning

Lastly let's look at Reinforcement learning the algorithms that knows how to play chess Go or Super Mario and on the other hand con control robots and optimize store layout

the base of this algorithm works on the premise we can act on the environment and we get the result of this action for example

the robot moves and then he receive change in gps positioning

we put the diapers next to the beers and see if sales go up or down

we tries different strategies in games to win the game

Can be used

to compress data

The issues in Reinforcement learning are :

It requires an interactive environment or a simulation of one

Sometime the knowledge on the results of the action are only partial this is called partially observed

Sometimes the result can be corrupted like the case of faulty sensors

In conclusion

When we understand what we can achieve with each type of ML and we know what is required to make it work the sky's the limit.

Thank you for reading if you have any question or suggestion please leave a comment or contact me at insightindevelopment@gmail.com

updated 8 years ago

The 3 Types of Machine Learning Algorithms

Insights Dev 2018-08-15 | Today we are going to talk about the 3 types of ML algorithms

how they work,what are their usage in the real world and what issues we face while using them.
see more at my blog:
insightsdev.com
What is ML the very short version

In short ML is the ability to take data and use it to create a modal we can use to infer the structure or properties of new records

the current buzz environment is looking at ML as data in magic out machine able to do anything

This create a lot of cases of unrealistic expectations and misuse in implementation.

By understanding the premise of each type of ML, when to use it and its limitations we can start a meaningful conversation on how to implement this abilities in real applications

So this is why it's important to distinguish between the type of ML algorithms

Because each type is helpful for certain kind of problems and use different kind of data and vary in the insight you can get from it

the 3 types of ML algorithms are

Supervised learning - When we know what we are trying to infer and we have data of the result

Unsupervised learning -When we don't know what we are trying to infer or we know but we don't have enough data about the expected result

Reinforcement Learning - When the environment change by the action of the algorithm

So Lets start looking at each one in details

Supervised learning

this is the usually known as prediction algorithms it will receive as an input data that for each record we know what is the expected results for that data

a basic example for this is knowing :

red 2016 Mazda cost 30K$

red 2017 Mazda cost 35K$

we predict a Red 2018 Mazda will cost 40K$

but supervised learning ist limited to prediction of a value

you can use it to create:

Using customer service Q&A to give the most proper response for chatbot

automatically Tag Social Media using what user tagged in the past

emotion recognition in form an image using what user said they felt at that moment

The Limit of this approach is that each record need to be labeled with the expected result

this is a problem when :

Not enough records or no history

we don't know result for each record

we don't know what are we looking or what we looking for is vary rear

Unsupervised learning

To cope with the cases we don't know what is the meaning of the data or its not labeled.

We can use unsupervised learning to understand the inner relation between the property of the data and find pattern in the data and anomalies that divert from this patterns

for example by looking on the transactions in a supermarket we can discover:

that people how buy diapers also by beers

understand what characterize the customers hoe buy beer

find anomalies in purchase that can lead to detecting fraud

this can give you insight of to the patterns an anomaly but it lacks in a few area

there is no indication if a pattern or anomaly is good or bad

if something is an anomaly it doesn't mean its fraud

Lots of false detection of anomaly and pattern

the result can vary a lot by changing the model definition( like group to 5 groups instead of 10)

Reinforcement learning

Lastly let's look at Reinforcement learning the algorithms that knows how to play chess Go or Super Mario and on the other hand con control robots and optimize store layout

the base of this algorithm works on the premise we can act on the environment and we get the result of this action for example

the robot moves and then he receive change in gps positioning

we put the diapers next to the beers and see if sales go up or down

we tries different strategies in games to win the game

Can be used

to compress data

The issues in Reinforcement learning are :

It requires an interactive environment or a simulation of one

Sometime the knowledge on the results of the action are only partial this is called partially observed

Sometimes the result can be corrupted like the case of faulty sensors

In conclusion

When we understand what we can achieve with each type of ML and we know what is required to make it work the sky's the limit.

Thank you for reading if you have any question or suggestion please leave a comment or contact me at insightindevelopment@gmail.com

reverse הנזיר שהריץ את האלגוריתם 2

Insights Dev 2019-02-28 | ...

reversim Proposal הנזיר שהריץ את האלגוריתם 1

Insights Dev 2019-02-28 | ...

Insights into the world of software development

Insights Dev 2018-08-09 | see the blog at
insightsdev.com

my company http://inelint.com

Hi my name is Yechiel Amsalem im 34 year old married with 2 boys.
I’m VP R&D of InElint security and in the last 12 years i have been helping organization from areas like health care, financial and manufacturing to incorporate the emerging technologies like Big data, machine learning, cyber and mobile apps.
Using this technologies to help improve the value they give their customers
The Problem (There is always a problem)
in the last few years with explosion of many emerging technology the gap between technical and business people grow in a rapid rate.
this leaves the business only with material that is water down
and create a environment of buzz without any content and context
and leave the business people without a real understanding of
what is the key concept of the technology
why is it novel about and why is it different from how we used to do
what are the advantages that we can apply to our orgnization
and most importantly what are the disadvantages and risks involved in using this technology

The solution
In Insights dev i will try to give you some Insights into the world of software development and remove the buzz so you can start using this concepts to help you improve your understanding and your business

Thank you for reading if you have any question or suggestion please leave a comment or contact me at insightindevelopment@gmail.com