Python – Solving Regression Problems | Other It & Software Online Course by Udemy

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Python for Regression Analysis

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Python – Solving Regression Problems

In this course, first students will learn the fundamental python modules necessary for Regression Analysis such as NumPy and matplotlib. Then a detailed theoretical and mathematical analysis of simple regression, multiple regression and polynomial regression are provided with examples, equations and derivations. The problems with simple regression, multiple regression and polynomial regression are solved using Least square method and Gradient descent. Finally all the concepts provided in the theory are implemented with Python. The most import concept of overfitting and generalization is explained with examples and then a generalization technique called Ridge Regression is explained in detail and implemented in Python. By completing this course students will be able to solve Regression problems using Least Square and method and will also be able to address the overfitting problem in Regression analysis using python. Following is the detailed course outline. Section 01: Introduction1.1: Target audience of the course.1.2: Topics covered in the course. Section 02: Important python Modules2.1: Dealing with arrays2.2: Plotting and visualizationSection 03: Least Square Regression3.1: Slope-intercept form of line.3.2: Definition of Regression.3.3: Multiple Regression.3.4: Least square (LS) solution of regression.3.5: Implementation of Simple Regression in python using LS.3.6: Implementation of Multiple Regression in python using LS.3.7: Polynomial Regression.3.8: Implementation of Polynomial Regression in python. Section 04: Regression by Gradient Descent4.1: Fundamentals of Gradient Descent.4.2: Pictorial explanation of Gradient Descent.4.3: Comparison of Gradient Descent with Least Square Solution.4.4: Solving Regression problem in python using Gradient Descent. Section 05: Overfitting and Regularization5.1: Concept of overfitting.5.2: Regularization-Addressing the problem of overfitting.5.3: Ridge Regression and comparison with LS regression.5.4: Comparison of Ridge and LS Regression using python.

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