ML-labs

This repository contains a collection of machine learning labs covering both theoretical and practical aspects of key ML algorithms and techniques.

Contents

The repository is organized into several labs, each focusing on a foundational topic in machine learning:

• Logistic Regression:
  Explore the basics of logistic regression for classification tasks, including model formulation, training, and evaluation.

• Regularization and Feature Selection:
  Learn how to combat overfitting using regularization techniques (such as L1 and L2), and how to select relevant features for better model performance.

• Decision Tree and Ensemble Learning:
  Understand decision tree construction, splitting criteria, pruning, and extend to ensemble methods like Random Forests and Gradient Boosting.

• Support Vector Machines (SVM):
  Dive into the theory and application of SVMs for linear and non-linear classification, including kernel methods.

• Naive Bayes:
  Study the principles behind Naive Bayes classifiers and apply them to real-world datasets.

• Neural Networks (NNs):
  Introduce the basics of neural networks, including forward and backward propagation, activation functions, and practical training.

Each lab typically includes:

• Theoretical explanations and derivations
• Practical coding exercises and implementations
• Visualizations and result analysis