This repository contains notes and code examples for an introductory machine learning course. The materials cover various machine learning algorithms and techniques, including linear regression, polynomial regression, logistic regression, and neural networks. The repository is organized into Jupyter notebooks and LaTeX notes for easy reference and study.
The repository is organized into the following directories:
introduction_to_machine_learning/: Contains Jupyter notebooks for different sessions of the machine learning course.latex_notes/: Contains LaTeX notes for the supervised learning part of the course.
The introduction_to_machine_learning/ directory contains the following Jupyter notebooks:
session-2-linear-regression-gradient-descent2.ipynb: Covers linear regression and gradient descent.session-3-polynomial-regression.ipynb: Covers polynomial regression.session-4-Linear Classification and Perceptron Neural Network.ipynb: Covers linear classification and perceptron neural networks.session-5-Logistic_Regression.ipynb: Covers logistic regression.session-6-KNN.ipynb: Covers kNN.
The latex_notes/ directory contains the following LaTeX files:
supervised.tex: Contains detailed notes on supervised learning.
To run the Jupyter notebooks, you need to have the following installed:
- Python 3.10.1 or later
- Jupyter Notebook
- NumPy
- Matplotlib
- Scikit-learn
You can install the required Python packages using the following command:
pip install numpy matplotlib scikit-learn