Description
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 so that you actually know what is happening all behind the scenes and that you know what other people are talking about. Also if you know the Math behind a machine learning model you will only then be able to make reliable, efficient, accurate and trustworthy model and this is actually how machine learning models are made because if you made a machine learning model which is not accurate that is going to cause so many blunders that your human brain can't even imagine. Accuracy is one of the most important factors while developing a machine learning model and that we have made machine learning models keeping the accuracy in mind.
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 gives you the highest accuracy rate.
You will also learn which machine learning model to use for a problem. There are so many machine learning models that you can use to solve a problem but choosing the one which will give you the highest accuracy is the most difficult and tedious work to do and we have focused and worked a lot of which model to choose in which problem.
I am looking forward to seeing you on the course.
What Will You Learn?
- 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
Requirements
- Basics of Python
Curriculum
Course consist of total 11h 17min of content, in total.
Section 1: Introduction
Section 2: Simple Linear Regression Intuition
Section 3: Simple Linear Regression in Python
Section 4: Multiple Linear Regression Intuition
Section 5: Multiple Linear Regression in Python
Section 6: Polynomial Regression Intuition
Section 7: Polynomial Regression in Python
Section 8: Ridge Regression Intuition
Section 9: Ridge Regression in Python
Section 10: Lasso Regression Intuition
Section 11: Lasso Regression in Python
Section 12: Decision Tree Regression Intuition
Section 13: Decision Tree Regression in Python
Section 14: Random Forest Regression Intuition
Section 15: Random Forest Regression in Python
Section 16: ++++++++++++++Beginning Classifications++++++++++++++
Section 17: K Nearest Neighbor Model Intuition
Section 18: K Nearest Neighbor Model in Python
Section 19: Logistic Regression Intuition
Section 20: Logistic Regression in Python
Section 21: Decision Tree Classification Intuition
Section 22: Decision Tree Classification in Python
Section 23: Random Forest Classification Intuition
Section 24: Random Forest Classification in Python
Section 25: Naiive Bayes Classification Intuition
Section 26: Naiive Bayes Classification in Python
Section 27: Support Vector Classification Intuition
Section 28: Support Vector Classification in Python
About the Instructor
I am a Software Engineer, Data Scientist and Entrepreneur. I started coding when i was only 12. I love teaching to people who are curious to learn. I think BitDegree is the platform which is going to be the hub of online education in the future so that is why i am here to help you guys who are curious to learn
I love to teach the newbies and also the professionals. I always try to teach something new and in-demand topics to my students. I teach with passion and purpose! Every course is delivered with my students in mind. I am here to change your Life!
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