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  • Theory and Maths of Regression Analysis
  • Solving Regression Problems With Python
  • Least Square Regression
  • Regression by Gradient Descent
  • Ridge Regression

The focus of the course is to solve Regression problem in python with the understanding of theory and Mathematics as well.

All the mathematical equations for Regression problem will be derived and during coding in python we will code these equations step by step to see the implementation of mathematics of Regression in python.

This course is for everyone. A high school student, a university student and
a researcher in machine learning.

The course starts from the fundamentals of Regression and then we will
move on to next levels with a decent pace so that every student can follow
along easily.

In this course you will learn about the theory of the Regression,
mathematics of Regression with proper derivations and following all the
steps. Finally, you will learn how to code Regression in python by following
the equations of Regression learned in the theory.

  • Basic Knowledge of Mathematics will be helpful.
  • Students learning Data Science and Machine Learning.
  • Want to switch from Matlab and Other Programming Languages to Python
  • Students and Researchers who knows about Regression Analysis but don't know how to implement in Python
  • Every individual who wants to learn Linear Regression from scratch
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  • Section 1 : Introduction 3 Lectures 00:05:12

    • Lecture 1 :
    • Introduction of the Course Preview
    • Lecture 2 :
    • Course Outline
    • Lecture 3 :
    • Course Material
    • Please download all the course material which has codes and lecture slides
  • Section 2 : Python Crash Course 16 Lectures 03:24:45

    • Lecture 1 :
    • Introduction of the Section
    • Lecture 2 :
    • Installing Python Packages
    • Lecture 3 :
    • Introduction of Jupyter Notebook
    • Lecture 4 :
    • Arithmetic With Python Part-01
    • Lecture 5 :
    • Arithmetic With Python Part-02
    • Lecture 6 :
    • Arithmetic With Python Part-03
    • Lecture 7 :
    • Dealing With Arrays Part-01
    • Lecture 8 :
    • Dealing With Arrays Part-02
    • Lecture 9 :
    • Dealing With Arrays Part-03
    • Lecture 10 :
    • Plotting and Visualization Part-01
    • Lecture 11 :
    • Plotting and Visualization Part-02
    • Lecture 12 :
    • Plotting and Visualization Part-03
    • Lecture 13 :
    • Plotting and Visualization Part-04
    • Lecture 14 :
    • Lists In Python
    • Lecture 15 :
    • For Loops Part-01
    • Lecture 16 :
    • For Loops Part-02
  • Section 3 : Regression By Least Square Method 14 Lectures 02:13:30

    • Lecture 1 :
    • Slope- Intercept Form
    • Lecture 2 :
    • Definition of Regression
    • Lecture 3 :
    • Multiple Regression
    • Lecture 4 :
    • Least Square Regression Part-01
    • Lecture 5 :
    • Least Square Regression Part-02
    • Lecture 6 :
    • Least Square Regression Part-03
    • Lecture 7 :
    • Simple Regression in Python Part-01
    • Lecture 8 :
    • Simple Regression in Python Part-02
    • Lecture 9 :
    • Multiple Regression In Python Part-01
    • Lecture 10 :
    • Multiple Regression In Python Part-02
    • Lecture 11 :
    • Multiple Regression In Python Part-03
    • Lecture 12 :
    • Polynomial Regression
    • Lecture 13 :
    • Polynomial Regression In Python
    • Lecture 14 :
    • Summary of Polynomial Regression
  • Section 4 : Regression By Gradient Descent 5 Lectures 00:30:09

    • Lecture 1 :
    • Introduction of Gradient Descent
    • Lecture 2 :
    • Pictorial Explanation of Gradient Descent
    • Lecture 3 :
    • Gradient Descent and Least Square Regression
    • Lecture 4 :
    • Gradient Descent in Python Part-01
    • Lecture 5 :
    • Gradient Descent in Python Part-02
  • Section 5 : Overfitting and Regularization 3 Lectures 00:30:14

    • Lecture 1 :
    • Introduction to Overfitting and Regularization
    • Lecture 2 :
    • Ridge Regression
    • Lecture 3 :
    • Ridge Regression in Python
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4 Courses

Dr. Zeeshan is PhD in Electrical and Computer Engineering from Toronto Metropolitan University. He has more than 18 years of teaching and research experience. He has taught many courses related to Computer Science and Computer Engineering. such as Machine Learning, Deep Learning, Image Processing, Computer Vision, Signal Processing and Python Programming. He has publications in reputed journals and conferences.
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