**Module 1: Introduction to Machine Learning**

**Week 1: Overview of Machine Learning**- What is Machine Learning?
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Real-world applications of Machine Learning
- Setting up Python environment: Anaconda, Jupyter Notebooks, essential libraries (NumPy, pandas, matplotlib, scikit-learn)

**Week 2: Python for Data Science**- Python basics: Data types, control flow, functions
- NumPy for numerical computing
- pandas for data manipulation
- Data visualization with matplotlib and seaborn

**Module 2: Supervised Learning**

**Week 3: Regression**- Introduction to regression analysis
- Simple Linear Regression
- Multiple Linear Regression
- Evaluation metrics: Mean Squared Error, R-squared

**Week 4: Classification**- Introduction to classification
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC

**Week 5: Advanced Supervised Learning Algorithms**- Decision Trees
- Random Forests
- Gradient Boosting Machines (XGBoost)
- Support Vector Machines (SVM)

**Module 3: Unsupervised Learning**

**Week 6: Clustering**- Introduction to clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN

**Week 7: Dimensionality Reduction**- Introduction to dimensionality reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Singular Value Decomposition (SVD)

**Module 4: Reinforcement Learning**

**Week 8: Fundamentals of Reinforcement Learning**- Introduction to Reinforcement Learning
- Key concepts: Agents, Environments, Rewards
- Markov Decision Processes (MDP)
- Q-Learning

**Week 9: Deep Reinforcement Learning**- Deep Q-Networks (DQN)
- Policy Gradient Methods
- Applications of Reinforcement Learning

**Module 5: Deep Learning**

**Week 10: Introduction to Neural Networks**- Basics of Neural Networks
- Activation Functions
- Training Neural Networks: Forward and Backward Propagation

**Week 11: Convolutional Neural Networks (CNNs)**- Introduction to CNNs
- CNN architectures: LeNet, AlexNet, VGG, ResNet
- Applications in Image Recognition

**Week 12: Recurrent Neural Networks (RNNs)**- Introduction to RNNs
- Long Short-Term Memory (LSTM) networks
- Applications in Sequence Prediction

**Module 6: Advanced Topics**

**Week 13: Natural Language Processing (NLP)**- Introduction to NLP
- Text Preprocessing
- Sentiment Analysis
- Topic Modeling

**Week 14: Model Deployment and Production**- Saving and loading models
- Introduction to Flask for API creation
- Deployment on cloud platforms (AWS, Google Cloud, Heroku)

**Week 15: Capstone Project**- Work on a real-world project
- End-to-end model development: Data collection, preprocessing, model training, evaluation, and deployment
- Presentation and review