MachineLearning

This repository contains a collection of practical labs covering various topics in machine learning. The labs are designed to provide hands-on experience with different machine learning algorithms and techniques. Each lab includes code examples, explanations, and exercises.

Contents

1. Regression

   • Linear Regression
   • Logistic Regression
   • Multinomial Regression (TP)

2. Data Preparation

   • Data Cleaning
   • Feature Engineering
   • Data Normalization and Scaling

3. Regularization

   • L1 Regularization
   • L2 Regularization
   • Elastic Net

4. Decision Trees

   • Building Decision Trees
   • Pruning Techniques

5. Random Forests

   • Ensemble Methods
   • Random Forest Classifier
   • Random Forest Regressor

6. Feature Selection

   • Univariate Selection
   • Recursive Feature Elimination
   • Principal Component Analysis (PCA)

7. Naive Bayes

   • Gaussian Naive Bayes
   • Multinomial Naive Bayes

8. Support Vector Machines (SVM)

   • SVM for Classification
   • SVM for Regression

9. Introduction to Neural Networks

   • Basic Concepts
   • Single Layer Perceptron

10. Backpropagation

    • Training Neural Networks
    • Gradient Descent

11. Advanced Neural Network Architectures

    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)

12. Reinforcement Learning

    • Basics of Reinforcement Learning (Note: This lab is not covered)