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The Complete Supervised Machine Learning Models In Python

The Complete Supervised Machine Learning Models In Python

That is the reason, I have covered the Math behind every model in the intuition part of each Model.

Overview

In this course, you are going to learn all types of Supervised Machine Learning Models implemented in Python. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.

Implementation in Python is done in such a way so that not only you learn how to implement a specific Model in Python but you learn how to build real times templates and find the accuracy rate of Models so that you can easily test different models on a specific problem, find the accuracy rates and then choose the one which give you the highest accuracy rate.

I am looking forward to see you in the course..

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Course Information

Basics of Python is required

Learn Complete Supervised Machine Learning Models in Python
Learn the Math behind every Machine Learning Model
Learn the Intuition of each Model
Learn to make simple and GUI Based Templates
Learn to choose the best Machine Learning Model for a specific problem

Anyone who wants to learn Supervised Machine Learning Models
Anyone who wants to learn the Math behind Machine Learning Models
Anyone curious about Data Science

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Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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