1. Foundations of Machine Learning
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Basic Terminology: Features, labels, model, training
2. Essential Mathematics for Machine Learning
- Linear Algebra: Vectors, matrices, matrix operations
- Probability and Statistics: Distributions, Bayes' theorem, expectation
- Calculus: Derivatives, partial derivatives, gradients
- Optimization: Cost functions, gradient descent
3. Data Preprocessing and Feature Engineering
- Data Cleaning: Handling missing values, outliers
- Feature Scaling: Normalization, standardization
- Feature Selection: Filtering, wrapper, and embedded methods
- Feature Engineering: Creating new features, encoding categorical data
4. Supervised Learning Algorithms
- Linear Regression and Logistic Regression
- Classification Algorithms: KNN, Decision Trees, Naive Bayes
- Support Vector Machines (SVM)
- Ensemble Methods: Random Forests, Gradient Boosting, AdaBoost
- Neural Networks: Basics, backpropagation
5. Model Evaluation and Validation
- Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC-AUC
- Regression Metrics: MAE, MSE, R-squared
- Cross-Validation: k-fold, leave-one-out
- Hyperparameter Tuning: Grid search, random search
6. Unsupervised Learning Algorithms
- Clustering: K-means, hierarchical clustering, DBSCAN
- Dimensionality Reduction: PCA, t-SNE, LDA
- Anomaly Detection: Isolation Forest, One-Class SVM
7. Advanced Machine Learning Techniques
- Deep Learning: CNNs, RNNs, Transformers
- Transfer Learning: Pre-trained models, fine-tuning
- Reinforcement Learning: MDP, Q-learning, Deep Q-Networks
- NLP: Tokenization, embeddings, transformers (BERT, GPT)
8. Regularization and Optimization Techniques
- Regularization: L1, L2, Elastic Net
- Optimization: SGD, Adam, Momentum
9. Time Series Analysis and Forecasting
- Basics: Trends, seasonality, stationarity
- Models: ARIMA, SARIMA, Exponential Smoothing
- Deep Learning for Time Series: LSTMs, GRUs
10. Model Deployment and Scaling
- Model Deployment: Batch vs. real-time, cloud options
- Serving Models: REST APIs, Flask, FastAPI, Docker
- Model Monitoring: Concept drift, performance tracking
11. Ethics and Fairness in AI
- Bias and Fairness: Types of bias, mitigation techniques
- Privacy: Differential privacy, data anonymization
- Accountability and Transparency