Machine Learning Master

Welcome to Machine Learning master. This site hosts explanations of machine learning concepts with just enough math and code. To understand the concepts better, various Machine Learning, Deep Learning projects with their code & README files can be found in the projects section. Links to various learning resources can be found as well. Make use of the navigation and search bar to guide your way through the site. This is an ongoing project. If you want to contribute, check out the contribution guidelines regarding the same.

Developed By NVS Yashwanth.

Contents

1. What is Machine Learning?
   • Arthur Samuel
   • Tom Mitchell
2. Types of Machine Learning
   • Supervised Learning
     • Classification Problems
     • Regression Problems
   • Unsupervised Learning
   • Reinforcement Learning
3. Applications of Machine Learning
4. Machine Learning Life Cycle
5. Bias-Variance
   • Bias-Variance Tradeoff
   • Model Fitting
6. Understanding Gradient Descent
   • Cost Function
   • Gradient Descent
   • Learning Rate α
   • Normalization
   • The optimization procedure
   • Types of Gradient Descent
     • Batch Gradient Descent
     • Stochastic Gradient Descent (SGD)
     • Mini-batch Gradient Descent
   • Beyond the first-order optimization
7. Learning Curves
8. Linear Regression
   • What is Linear Regression?
   • Simple Linear Regression
   • Multiple Linear Regression
   • Polynomial Linear Regression
   • Cost Function
   • Training methods of linear regression models
     • Normal Equation
     • Pseduoinverse using SVD
     • Gradient Descent
   • A Polynomial Regression example
9. Regualrization Models
10. Linear Regression Assumptions
11. Logistic Regression

MORE CONTENT COMING SOON!

Projects

1. Machine Learning Projects
2. Deep Learning Projects

Resources

• Books
• Topic Wise Links
• Websites

Contribution Guidelines

• Developers
• How to contribute?
• When to contribute?

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