| Module | Topics |
|---|
| Introduction to AI & ML | History of AI, Basics of Artificial Intelligence and Machine Learning, Difference between AI, ML, and Deep Learning |
| Python Programming | Python Basics, Data Structures, Functions, Libraries for AI & ML (NumPy, Pandas, Matplotlib) |
| Statistics for ML | Descriptive Statistics, Inferential Statistics, Probability, Distributions, Hypothesis Testing |
| Data Preprocessing | Data Cleaning, Data Transformation, Handling Missing Values, Feature Scaling, Encoding Categorical Data |
| Supervised Learning | Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines |
| Unsupervised Learning | K-means Clustering, Hierarchical Clustering, Principal Component Analysis |
| Neural Networks and Deep Learning | Basics of Neural Networks, Deep Learning Frameworks, Convolutional Neural Networks, Recurrent Neural Networks |
| Natural Language Processing | Text Preprocessing, Bag of Words, TF-IDF, Word Embeddings, Sentiment Analysis |
| Reinforcement Learning | Introduction to Reinforcement Learning, Q-Learning, Policy Gradients, Deep Q Networks |
| AI & ML Projects | Project Lifecycle, Data Collection, Model Building, Evaluation, Deployment |
