Implementations of fundamental AI algorithms and concepts.
- Agents: Reflex, model, goal, and utility-based agents.
- Uninformed Search: BFS, DFS, IDS, UCS.
- Informed Search: A*, GBFS.
- Local Search: Hill Climbing, Genetic Algorithms, and Beam Search.
- CSP: Constraint Satisfaction using OR-Tools (N-Queens, Scheduling, Graphs).
- Adversarial Search: Minimax algorithm and Alpha-Beta Pruning.
- Bayesian Networks: Probabilistic inference using pgmpy.
- Supervised Learning: Linear Regression, Decision Trees, and Support Vector Machines (SVM).
- Unsupervised Learning: K-Means Clustering.
- EDA: Exploratory Data Analysis using Pandas, Matplotlib, and Seaborn.