Module 1: Introduction to Machine Learning
- What is machine learning? Definitions and scope
- Types of learning: supervised, unsupervised, reinforcement
- Key concepts: training, testing, validation, overfitting, underfitting
- Real-world applications: recommendation systems, fraud detection, image recognition
Module 2: Data Preparation
- Data collection and preprocessing
- Handling missing values, normalization, scaling
- Feature engineering and selection
- Splitting datasets for training and testing
Module 3: Supervised Learning
- Regression: linear regression, polynomial regression
- Classification: logistic regression, decision trees, random forests
- Evaluation metrics: accuracy, precision, recall, F1-score, ROC curves
- Case study: predicting housing prices
Module 4: Unsupervised Learning
- Clustering: k-means, hierarchical clustering, DBSCAN
- Dimensionality reduction: PCA, t-SNE
- Applications: customer segmentation, anomaly detection
- Case study: grouping customers by behavior
Module 5: Neural Networks and Deep Learning
- Basics of neural networks: perceptrons, activation functions
- Deep learning architectures: CNNs, RNNs, transformers
- Training deep networks: backpropagation, optimization algorithms
- Case study: image classification with CNNs
Module 6: Reinforcement Learning
- Agents, environments, rewards, policies
- Q-learning and deep Q-networks (DQN)
- Applications: robotics, gaming, autonomous systems
- Case study: training an agent to play a simple game
Module 7: Tools and Frameworks
- Python libraries: Scikit-learn, TensorFlow, PyTorch, Keras
- Data handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- Hands-on labs: building ML models with these tools
Module 8: Challenges and Risks
- Bias and fairness in ML models
- Interpretability and explainability (XAI)
- Scalability and computational challenges
- Security risks: adversarial attacks
Module 9: Ethics and Governance
- Responsible AI practices
- Regulatory frameworks: GDPR, CCPA, NDPR
- Transparency in ML decision-making
- Building trust with stakeholders
Module 10: Future Trends
- Generative AI and large language models
- AutoML and automated feature engineering
- ML in edge computing and IoT
- Quantum machine learning