Machine Learning

Hy,

As in my last post I had explained you about What is Artificial Intelligence? So in that post ,one thing was clear that to learn to create an AI first we have to learn to create Machine Learning Models which is the most basic requirement for creating an AI.

So, a question arise that what are Machine Learning Models?

In my last post I have already explained Machine Learning in brief .So now we know that Machine Learning is training the machine to make prediction on the basis of Labels and Features.

So in Python how to create a Machine Learning model?

Ans: There are many ways to create a Machine Learning model but the simplest way is to use modules or packages like Sci-Kit Learn ,Tensorflow and Numpy.

There are many other Modules to create Machine Learning models but why did I recommend you only these modules.

The answer lies in their user-friendly interface .These are the most simple to use modules.

TensorFlow       

This is a module created by Google for AI development .This module is used for both Machine Learning as well as Deep Learning.

For more information visit Tensorflow

Sci-Kit Learn

This module is used in making Regression and Classification Softwares .What Regression and Classification is ?I will tell you in my next post.

For more information visit SciKit Learn

Numpy      
This module is used for making matrix array. It can also compress the data in a dataset for making the dataset executable in machines which are of less RAM and Storage.

For more information visit Numpy


OpenCV

This module is used for making real time Image Detection and Object Detection. This module uses webcam of your P.C. and works to detect the specific object you train it for.

For more information visit OpenCV


In my next post I'm going to tell you about what is Regression and Classification and after that I will give practical knowledge to you through videos.

I upload post on daily basis So don't forget to Like and Follow my Blog and subscribe if you want to be notified the moment I upload a post.

Thank you

Samvrit Tiwari

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