Skip to content

README.md

Latest commit

 

History

History
57 lines (43 loc) · 1.5 KB

README.md

File metadata and controls

57 lines (43 loc) · 1.5 KB

🤖 ML Lab Programs

A collection of Machine Learning lab programs implemented in Python (Jupyter Notebook) covering fundamental supervised and unsupervised learning algorithms, search algorithms, and neural network concepts.

📚 Programs Included

Notebook Description
Bagging&Boosting.ipynb Implementation of ensemble learning techniques: Bagging and Boosting
Bayesian_Network.ipynb Bayesian Network model implementation
FOIL.ipynb First Order Inductive Learner algorithm
Find_S_Algorithm.ipynb Candidate Elimination using Find-S concept learning algorithm
K_means_algo.ipynb K-Means Clustering implementation
SOM.ipynb Self Organizing Map neural network implementation
candidate_elimination.ipynb Candidate Elimination algorithm for concept learning

🚀 Technologies Used

  • Python
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib

🎯 Learning Outcomes

Through these implementations, I explored:

  • Supervised Learning Algorithms
  • Unsupervised Learning Techniques
  • Ensemble Learning Methods
  • Concept Learning Algorithms
  • Neural Networks Basics
  • Probabilistic Graphical Models

📂 Repository Structure

ML_lab_programs/
│
├── Bagging&Boosting.ipynb
├── Bayesian_Network.ipynb
├── FOIL.ipynb
├── Find_S_Algorithm.ipynb
├── K_means_algo.ipynb
├── SOM.ipynb
├── candidate_elimination.ipynb
└── README.md