| Module | Topics |
|---|---|
| Introduction to AL & ML | History of AL & ML, Basics Concepts, Differences between AL & ML |
| Python for AL & ML | Python Basics, Libraries for AL & ML (NumPy, Pandas, Matplotlib, Scikit-learn) |
| Data Preprocessing | Data Cleaning, Data Transformation, Handling Missing Values, Feature Scaling |
| Supervised Learning | Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines |
| Unsupervised Learning | K-means Clustering, Hierarchical Clustering, PCA (Principal Component Analysis) |
| Deep Learning | Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, TensorFlow and Keras |
| Natural Language Processing (NLP) | Text Preprocessing, Word Embeddings, Sentiment Analysis, Language Models |
| Reinforcement Learning | Introduction to Reinforcement Learning, Q-learning, Policy Gradients, Deep Q-Networks (DQN) |
| AL & ML Project | Project Planning, Dataset Collection, Model Building, Evaluation, Deployment |
