• Overview of machine learning and its applications
• Different types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
• Understanding the machine learning workflow
• Key concepts: features, labels, training, and testing

Module 2: Data Preprocessing and Exploration

• Data collection and preprocessing techniques
• Handling missing data and outliers
• Feature scaling and normalization
• Exploratory data analysis and visualization
• Feature engineering and selection

Module 3: Supervised Learning Algorithms

• Linear regression
• Logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Naive Bayes classifiers

Module 4: Unsupervised Learning Algorithms

• Clustering algorithms (k-means, hierarchical clustering)
• Dimensionality reduction techniques (Principal Component Analysis, t-SNE)
• Association rule mining
• Anomaly detection
• Recommender systems

Module 5: Neural Networks and Deep Learning

• Introduction to neural networks
• Activation functions and network architectures
• Training neural networks (backpropagation, gradient descent)
• Convolutional Neural Networks (CNNs) for image classification
• Recurrent Neural Networks (RNNs) for sequence data

Module 6: Natural Language Processing (NLP)

• Text preprocessing techniques
• Bag-of-Words and TF-IDF representations
• Sentiment analysis and text classification
• Named Entity Recognition (NER)
• Language modeling and sequence generation

Module 7: Reinforcement Learning

• Introduction to reinforcement learning
• Markov Decision Processes (MDPs)
• Q-learning and Deep Q-learning
• Policy gradient methods
• Applications of reinforcement learning

Module 8: Model Evaluation and Validation

• Evaluation metrics (accuracy, precision, recall, F1 score)
• Cross-validation techniques
• Overfitting and regularization methods
• Hyperparameter tuning and optimization
• Model selection and ensemble learning

Module 9: Model Deployment and Productionization

• Model deployment strategies (cloud, containers)
• RESTful APIs for model serving
• Continuous integration and deployment (CI/CD)
• Monitoring and performance evaluation
• Ethical considerations in machine learning

Module 10: Capstone Project and Practical Applications

• Real-world machine learning project implementation
• Capstone project focusing on designing, developing, and deploying a machine learning model
• Presentation and documentation of the project