Python For Simple, Multiple And Polynomial Regression Models
Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.76 GB | Duration: 6h 43m
Published 10/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.76 GB | Duration: 6h 43m
Complete Linear Regression Analysis - Theory, Intuition, Mathematics and Implementation in Python.
What you'll 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
Requirements
Basic Knowledge of Mathematics will be helpful
Description
•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 anda researcher in machine learning.• The course starts from the fundamentals of Regression and then we willmove on to next levels with a decent pace so that every student can followalong easily.• In this course you will learn about the theory of the Regression,mathematics of Regression with proper derivations and following all thesteps. Finally, you will learn how to code Regression in python by followingthe equations of Regression learned in the theory.Who this course is for ?Students learning Data Science, Machine Learning and Applied Statistical Analytics.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 Linear Regression Analysis from scratch.
Overview
Section 1: Introduction
Lecture 1 Introduction of the Course
Lecture 2 Course Outline
Lecture 3 Course Material
Section 2: Python Crash Course
Lecture 4 Introduction of the Section
Lecture 5 Installing Python Package
Lecture 6 Introduction of Jupyter Notebook
Lecture 7 Arithmetic With Python Part-01
Lecture 8 Arithmetic With Python Part-02
Lecture 9 Arithmetic With Python Part-03
Lecture 10 Dealing With Arrays Part-01
Lecture 11 Dealing With Arrays Part-02
Lecture 12 Dealing With Arrays Part-03
Lecture 13 Plotting and Visualization Part-01
Lecture 14 Plotting and Visualization Part-02
Lecture 15 Plotting and Visualization Part-03
Lecture 16 Plotting and Visualization Part-04
Lecture 17 Lists In Python
Lecture 18 for loops Part-01
Lecture 19 for loops Part-02
Section 3: Regression Analysis by Least Square Method
Lecture 20 Slope-Intercept Form
Lecture 21 Definition of Regression
Lecture 22 Multiple Regression
Lecture 23 Least Square Regression Part-01
Lecture 24 Least Square Regression Part-02
Lecture 25 Least Square Regression Part-03
Lecture 26 Simple Regression in Python Part-01
Lecture 27 Simple Regression in Python Part-02
Lecture 28 Multiple Regression in Python Part-01
Lecture 29 Multiple Regression in Python Part-02
Lecture 30 Multiple Regression in Python Part-03
Lecture 31 Polynomial Regression
Lecture 32 Polynomial Regression in Python
Lecture 33 Summary of Polynomial Regression
Section 4: Regression By Gradient Descent
Lecture 34 Introduction of Gradient Descent
Lecture 35 Pictorial Explanation of Gradient Descent
Lecture 36 Gradient Descent and Least Square Regression
Lecture 37 Gradient Descent in Python Part-01
Lecture 38 Gradient Descent in Python Part-02
Section 5: Overfitting and Regularization
Lecture 39 Introduction to Overfitting and Regularization
Lecture 40 Ridge Regression
Lecture 41 Ridge Regression in Python
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