English | Size: 388 MB
Genre: eLearning
Linear Regression Model Project in Python for Beginners Part 1, Regression is one of the foundational techniques in Machine Learning. Being one of the most well-understood algorithms, beginners always struggle to understand some fundamental terminology related to regression. In this series of projects, we try to give you basic ideas of underlying concepts with the help of practical examples. If you are starting your career or want to brush up on your knowledge of regression, this course is made up for you. This project begins by introducing some simple real-life examples for regression. From a brief introduction to most of the concepts used in regression to hands-on experience, this project will give you enough understanding to apply those in real-world problems. With the help of the background developed, you will code your regression model in python.
This project starts with a real-life example for regression analysis, with an introduction to simple and multiple linear regression. Building the statistical foundation for the regression, it gives you a brief idea of the formula of regression. With this background, the first regression model in python is built. Going through the interpolation and extrapolation also explains errors in regression and Lurking variables. The point estimators of mean and variance and distributions of underlying parameters are also discussed. The coefficient of determination is also known, and R squared is briefly explained. The project ends with diagnostics and remedial measures for regression with a practical explanation.
What you’ll learn
What is Regression?
Types of Regression
What is Mean, Variance, and Standard Deviation?
Correlation and Causation
What are Observational and Experimental data?
Formula for Regression
Building a Simple Linear Regression model
Understanding Interpolation and Extrapolation
What are Lurking Variables?
Derivation for Least Square Estimates
The Gauss Markov Theorem
Point estimators of Regression
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