Python for Simple, Multiple and Polynomial Regression Models

Python for Simple, Multiple and Polynomial Regression Models, available at $19.99, has an average rating of 4.5, with 41 lectures, based on 2 reviews, and has 11 subscribers.

You will learn about Python Programming for Regression Analysis Mathematics and Intuition behind Regression Models Simple Linear Regression Multiple Linear Regression Polynomial Regression Ridge Regression Least Square Regression Regression by Gradient Descent This course is ideal for individuals who are Students learning Data Science and Machine Learning. or Want to switch from Matlab and Other Programming Languages to Python or Students and Researchers who knows about Regression Analysis but don't know how to implement in Python or Every individual who wants to learn Linear Regression from scratch It is particularly useful for Students learning Data Science and Machine Learning. or Want to switch from Matlab and Other Programming Languages to Python or Students and Researchers who knows about Regression Analysis but don't know how to implement in Python or Every individual who wants to learn Linear Regression from scratch.

Enroll now: Python for Simple, Multiple and Polynomial Regression Models

Summary

Title: Python for Simple, Multiple and Polynomial Regression Models

Price: $19.99

Average Rating: 4.5

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Python Programming for Regression Analysis
Mathematics and Intuition behind Regression Models
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Ridge Regression
Least Square Regression
Regression by Gradient Descent

Who Should Attend

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

Target Audiences

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

•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.

Who this course is for ?

Students learning Data Science, Machine Learning and Applied Statistical Analytics.

Students and Researchers who want to switch from Matlab and Other Programming Languages to Python.

Students and Researchers who know about the theory of Regression Analysis but don’t know how to implement in Python.

Every individual who wants to learn Simple, Multiple and Polynomial Regression Analysis from scratch.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction of the Course

Lecture 2: Course Outline

Lecture 3: Course Material

Chapter 2: Python Crash Course

Lecture 1: Introduction of the Section

Lecture 2: Installing Python Package

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

Chapter 3: Regression Analysis by Least Square Method

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

Chapter 4: Regression By Gradient Descent

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

Chapter 5: Overfitting and Regularization

Lecture 1: Introduction to Overfitting and Regularization

Lecture 2: Ridge Regression

Lecture 3: Ridge Regression in Python

Instructors

Zeeshan Ahmad
Machine Learning and Statistical Signal Processing

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4 stars: 2 votes
5 stars: 0 votes

Frequently Asked Questions

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