Data Science Blog Series
0. Foundations & Orientation Live
1. Mathematics for Data Science Live
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1.1 Linear Algebra
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1.2 Probability & Statistics
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1.3 Calculus & Optimization
2. Data Preprocessing & Feature Engineering Live
3. Core Machine Learning Algorithms Ongoing
3.1 Regression — Linear, Regularized, Robust & Advanced Parametric Models
- 3.1.1 Linear Regression (OLS): Foundations, Assumptions, and Interpretation
- 3.1.2 Regularized Linear Models: Ridge, Lasso, Elastic Net Explained
- 3.1.3 Generalized Linear Models (GLMs): Logistic, Poisson & Beyond
- 3.1.4 Evaluation Metrics for Regression: MAE, RMSE, R², and More
- 3.1.5 Robust Regression: Huber, RANSAC, and Quantile Methods
- 3.1.6 Capturing Non-Linearity: Polynomial, Splines & Basis Expansions
- 3.1.7 Advanced Parametric Regression: Bayesian Models, Mixed Effects, GPs
- 3.1.8 Optimization Techniques for Regression: Gradient Descent, Normal Equations, SGD and Beyond
3.2 Classification — Foundations, Algorithms, Evaluation & Deployment
- 3.2.1 Foundations of Classification: Problem Framing, Data Prep & Label Encoding Soon
- 3.2.2 Classical Algorithms Part 1: Logistic Regression & Naïve Bayes Classifiers Soon
- 3.2.3 Classical Algorithms Part 2: k-Nearest Neighbors (k-NN) & Support Vector Machines (SVM) Soon
- 3.2.4 Advanced Models Part 1: Softmax Regression & Linear Discriminant Analysis (LDA) Soon
- 3.2.5 Advanced Models Part 2: Quadratic Discriminant Analysis (QDA) & Probabilistic Graphical Models (PGMs) Soon
- 3.2.6 Model Evaluation & Diagnostics: Confusion Matrix, ROC-AUC, PR Curves Soon
- 3.2.7 Optimization & Training: Loss Functions, Gradient Descent, Regularization Soon
- 3.2.8 Real-World Deployment & Interpretability: SHAP, LIME & Model Explainability Soon
3.3 Tree-Based & Ensemble Learning — Decision Trees, Bagging, Boosting
3.4 Unsupervised Learning — Clustering, Dimensionality Reduction, Anomaly Detection
- 3.4.1 Clustering Algorithms: k-Means, DBSCAN, Hierarchical Clustering Soon
- 3.4.2 Dimensionality Reduction & Embeddings: PCA, t-SNE, UMAP Soon
- 3.4.3 Anomaly Detection Techniques: Statistical, Distance-Based, Isolation Forest Soon
- 3.4.4 Association Rule Learning: Apriori, FP-Growth, Market Basket Analysis Soon
- 3.4.5 Recommender Systems (Bridge Topic): Collaborative Filtering & Hybrid Models Soon
3.5 Probabilistic & Generative Models — MLE, MAP, EM, HMMs, CRFs
- 3.5.1 Maximum Likelihood & MAP Estimation: Foundations of Probabilistic Modeling Soon
- 3.5.2 Expectation-Maximization (EM): Latent Variables & Gaussian Mixture Models Soon
- 3.5.3 Hidden Markov Models (HMM): Sequence Modeling with Forward-Backward Algorithm Soon
- 3.5.4 Conditional Random Fields (CRF): Structured Prediction for NLP & Vision Soon
4. Model Evaluation, Hyperparameter Tuning & Experimentation — CV, Metrics, Interpretability, A/B Testing Coming
- 4.1 Cross-Validation Strategies: k-Fold, Time-Series CV, and Nested Validation
- 4.2 Metric Selection: Evaluation Metrics for Classification, Regression, Ranking, and More
- 4.3 Hyperparameter Optimization: Grid Search, Bayesian Tuning, AutoML
- 4.4 Model Interpretability: SHAP, LIME, PDP, Feature Attribution
- 4.5 A/B Testing & Experimentation: Design, Power Analysis, CUPED
- 4.6 Fairness & Bias Mitigation: Metrics, Algorithms, and Case Studies
5. Deep Learning — Architectures, Generative Models, and Graph ML Coming
5.1 Neural Network Fundamentals — Architecture, Training & Regularization
- 5.1.1 Perceptron & Feedforward Neural Networks: Basics of Deep Learning
- 5.1.2 Training Neural Networks: Backpropagation, Optimizers, Initialization
- 5.1.3 Regularization in Neural Networks: Dropout, Weight Decay, BatchNorm
- 5.1.4 Loss Functions: Cross-Entropy, MSE, Focal Loss and Use Cases
- 5.1.5 Code Walkthrough: MNIST Classification with MLP (PyTorch/Keras)
5.2 Deep Learning Architectures — CNNs, RNNs, and Transformers
- 5.2.1 Convolutional Neural Networks (CNNs): Vision Models & Feature Hierarchies
- 5.2.2 Recurrent Neural Networks (RNNs) and Sequence Models: LSTM, GRU, Bidirectional Models
- 5.2.3 Attention Mechanisms & Transformers: Self-Attention, BERT, GPT
5.3 Generative Deep Learning — Autoencoders, VAEs, and GANs
- 5.3.1 Autoencoders: Dimensionality Reduction, Denoising, Anomaly Detection
- 5.3.2 Variational Autoencoders (VAE): Latent Space Learning & Sample Generation
- 5.3.3 Generative Adversarial Networks (GANs): Image Synthesis & Applications
- 5.4 Graph Machine Learning: GCNs, GraphSAGE, GAT for Networked Data
6. Specialized Topics in Data Science — Recommenders, Time Series, NLP, Vision, RL Coming
- 6.1 Recommender Systems: Collaborative Filtering, Hybrid Models & Ranking Metrics
- 6.2 Time-Series Forecasting: ARIMA, XGBoost, LSTM & Transformer-Based Models
- 6.3 Bayesian & Probabilistic ML: MCMC, Variational Inference, Uncertainty Modeling
- 6.4 Natural Language Processing (NLP): Embeddings, Transformers & Classification
- 6.5 Computer Vision: Detection, Segmentation & Transfer Learning
- 6.6 Reinforcement Learning & Bandits: Q-Learning, PPO, and Exploration Strategies
7. Production Deployment & Monitoring — Pipelines, Drift, Serving, CI/CD Coming
- 7.1 From Prototype to Production: Packaging & Testing ML Models
- 7.2 Pipelines & Serialization: MLflow, ONNX, Scikit-Learn Pipelines
- 7.3 Deployment Strategies: REST APIs, Docker, Real-Time vs Batch Inference
- 7.4 Model Serving: FastAPI, TorchServe, GCP Vertex, AWS SageMaker
- 7.5 Monitoring & Maintenance: Drift Detection, Retraining, Shadow Testing
- 7.6 Scalability & Optimization: Distributed Inference, Quantization, ONNX Runtime
- 7.7 Infrastructure Challenges: DevOps, Cloud Platforms, MLOps Tooling
8. Applied ML in Practice — End-to-End Projects in Healthcare, Finance & E-commerce Coming
- 8.1 End-to-End Project Workflow: From Problem Framing to Deployment
- 8.2 Case Study: Healthcare — Disease Risk Prediction Using EHR & Clinical Features
- 8.3 Case Study: Finance — Real-Time Credit Card Fraud Detection System
- 8.4 Case Study: E-commerce — Personalized Product Recommender
- 8.5 Best Practices & Lessons Learned: Simplicity, Interpretability & Domain Knowledge
9. Responsible AI — Ethics, Fairness, Privacy, Governance & Safety Coming
- 9.1 Ethical Considerations & Bias: Algorithmic Fairness & Group-Level Equity
- 9.2 Transparency & Explainability: Model Cards, SHAP, LIME
- 9.3 Accountability: Human Oversight, Review Loops & Fail-Safe Systems
- 9.4 Privacy & Data Protection: GDPR, Differential Privacy, Federated Learning
- 9.5 Security of ML Systems: Adversarial Attacks, Data Poisoning & Model Inversion
10. Emerging & Advanced Topics — Transfer Learning, AutoML, Privacy-Preserving AI Coming
- 10.1 Transfer Learning: Fine-Tuning Pretrained Models like BERT & ResNet
- 10.2 Federated & Privacy-Preserving ML: Secure Aggregation & Differential Privacy
- 10.3 Automated Machine Learning (AutoML): Model Search, Tuning, and Deployment