🤖 AI/ML Lab Experiments

Hands-on AI & Machine Learning implementations and experiments

A structured lab notebook of AI/ML experiments — covering core algorithms, intelligent agents, and real-world problem solving.

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🧪 Lab Modules

Module 1 — Search Algorithms

Experiment	Description
BFS / DFS	Graph traversal for state-space search
A* Search	Heuristic-based pathfinding
Hill Climbing	Local search optimization

Module 2 — Knowledge & Reasoning

Experiment	Description
Propositional Logic	Truth tables, inference rules
First-Order Logic	Predicates, quantifiers
Bayesian Networks	Probabilistic reasoning under uncertainty

Module 3 — Machine Learning

Experiment	Description
Linear Regression	Predict continuous values
Decision Trees	Rule-based classification
Naïve Bayes	Probabilistic classification
K-Nearest Neighbors	Instance-based learning
K-Means Clustering	Unsupervised grouping

Module 4 — Neural Networks

Experiment	Description
Perceptron	Single-layer learning
Backpropagation	Multi-layer training from scratch
Activation Functions	Sigmoid, ReLU, Tanh comparison

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🛠️ Tech Stack

• Language: Python 3.10+
• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib
• Environment: Jupyter Notebook

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/Bhavan790/Aiml-Lab.git
cd Aiml-Lab

2. Install dependencies

pip install numpy pandas scikit-learn matplotlib seaborn jupyter

3. Launch Jupyter

jupyter notebook

Navigate to any module folder and open the .ipynb file.

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📁 Folder Structure

Aiml-Lab/
├── Module1_Search/
│   ├── bfs_dfs.ipynb
│   ├── astar.ipynb
│   └── hill_climbing.ipynb
├── Module2_Knowledge/
│   ├── propositional_logic.ipynb
│   └── bayesian_network.ipynb
├── Module3_ML/
│   ├── linear_regression.ipynb
│   ├── decision_tree.ipynb
│   ├── naive_bayes.ipynb
│   └── kmeans.ipynb
├── Module4_NeuralNets/
│   ├── perceptron.ipynb
│   └── backpropagation.ipynb
└── README.md

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💡 What I Learned

• How intelligent agents make decisions using search and heuristics
• The math behind Bayesian reasoning and probability in AI
• Comparing supervised vs unsupervised learning approaches
• Building a neural network from scratch — no frameworks, just NumPy
• How evaluation metrics differ across problem types

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🗺️ Roadmap

• Search algorithms (BFS, DFS, A*)
• Classical ML (Regression, Classification, Clustering)
• Neural networks from scratch
• Natural Language Processing experiments
• Reinforcement Learning basics (Q-learning)
• Computer Vision with OpenCV

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👨‍💻 Author

Bhavan Kumar RT — B.E. Electrical & Electronics, Rajalakshmi Engineering College

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