Machine Learning Fundamentals

Welcome to the Machine Learning Fundamentals repository! This collection of Jupyter notebooks covers essential concepts and techniques in machine learning, designed to help you grasp the foundations of this exciting field.

Table of Contents

1. Linear Regression

   • Introduction to linear regression and its applications
   • Implementing linear regression from scratch
   • Understanding cost functions and their role in optimization
   • Examples of common cost functions
   • Deep dive into the gradient descent optimization algorithm
   • Practical examples and variations of gradient descent
   • The importance of feature scaling in preprocessing
   • Standardization and normalization techniques
   • Techniques to create informative features from raw data
   • Real-world examples and best practices
   • Strategies for optimizing the learning rate in machine learning
   • Balancing convergence speed and stability

2. Linear Algebra

   • Solving systems of linear equations
   • Linear algebra methods for equation solving
   • Exploring key linear algebra concepts in machine learning
   • Matrix operations, vector spaces, and more
   • Leveraging vectorization for efficient computation
   • Performance improvements in machine learning

3. Linear Regression Toolkit

   • Predefined implementations of common linear regression methods
   • Easily apply linear regression to your datasets

Getting Started

To get the most out of these notebooks, make sure you have Jupyter Notebook installed on your local machine. You can install it using the following command:

pip install notebook