A bilingual, notebook-first machine learning course delivered in Jupyter Notebook (.ipynb) format. The repository is designed as a structured, chapter-by-chapter curriculum for learners who want a practical and theoretical path through classical machine learning, model evaluation, time series, reliability, MLOps, and applied case studies.
Each lesson is provided in:
- English:
Tutorials/English/... - Persian (Farsi / فارسی):
Tutorials/Persian/...with Persian notebooks ending in_Fa.ipynb
The English and Persian notebooks cover the same material, with the Persian version being a translation of the English content.
Datasets/
Classification/ # tabular datasets for classification examples
Regression/ # tabular datasets for regression examples
Clustering/ # datasets for unsupervised learning and clustering
Tutorials/
English/
Chapter1/
Chapter1_Lesson1.ipynb
Chapter1_Lesson2.ipynb
...
...
Chapter37/
Chapter37_Lesson1.ipynb
Chapter37_Lesson2.ipynb
...
Persian/
Chapter1/
Chapter1_Lesson1_Fa.ipynb
Chapter1_Lesson2_Fa.ipynb
...
...
Chapter37/
Chapter37_Lesson1_Fa.ipynb
Chapter37_Lesson2_Fa.ipynb
...
css/
rtl.css # RTL styles used by Persian notebooks
- Datasets/ contains CSV files used in examples, exercises, and projects.
- Tutorials/ contains the notebooks organized by Chapter → Lesson.
- css/rtl.css supports right-to-left rendering for Persian notebooks.
This curriculum contains:
- 37 chapters
- 330 lessons
- Introductory, advanced, and comprehensive extension modules
- End-to-end capstone projects for applied classical machine learning
This course spans an end-to-end classical machine learning curriculum, progressing from foundations to advanced topics and applied case studies:
- Machine learning foundations, problem formulation, workflow design, and reproducibility
- Data preprocessing, feature engineering, leakage prevention, and pipeline hygiene
- Exploratory data analysis, diagnostics, statistical testing, and data-quality forensics
- Supervised learning: regression, classification, GLMs, regularization, robust methods, and cost-sensitive learning
- Decision trees, tree variants, ensemble learning, boosting, calibration, and imbalanced learning
- SVMs, kernel methods, instance-based learning, and probabilistic models
- Unsupervised learning, clustering, dimensionality reduction, and association rules
- Cross-validation, model evaluation, uncertainty estimation, and decision analysis
- Time series modeling, forecasting, rolling validation, and anomaly detection
- Bayesian networks, graphical models, Gaussian processes, causal inference, and experimentation
- Fairness, privacy, robustness, security, reliability, conformal prediction, and MLOps
- Scalable, online, specialized, and capstone machine learning workflows
- Lesson 1: What is Machine Learning?
- Lesson 2: Types of Machine Learning (Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning)
- Lesson 3: Applications and Real-World Use Cases
- Lesson 4: ML Workflow (Data, Model, Evaluation, Deployment)
- Lesson 5: History and Evolution of ML
- Lesson 6: Key Mathematical Foundations (Linear Algebra, Probability, Optimization)
- Lesson 7: Common Misconceptions and Challenges in ML
- Lesson 8: ML vs. Statistics vs. Data Mining vs. AI (Boundaries and Overlaps)
- Lesson 9: Problem Formulation (Inputs/Outputs, Objective, Constraints, Costs)
- Lesson 10: Learning Paradigms and Task Taxonomy (Regression, Classification, Ranking, Forecasting)
- Lesson 11: What “Good Performance” Means (Generalization, Robustness, Reliability)
- Lesson 12: Reproducibility Basics (Random Seeds, Determinism, Experiment Tracking Concepts)
- Lesson 1: Understanding Data Types and Structures
- Lesson 2: Data Cleaning and Missing Values
- Lesson 3: Data Transformation and Encoding
- Lesson 4: Feature Scaling (Normalization & Standardization)
- Lesson 5: Handling Outliers and Imbalanced Data
- Lesson 6: Feature Engineering Fundamentals
- Lesson 7: Data Leakage and Prevention Techniques
- Lesson 8: Data Collection and Label Quality (Noise, Ambiguity, Measurement Error)
- Lesson 9: Missingness Mechanisms (MCAR, MAR, MNAR) and Practical Implications
- Lesson 10: Imputation Methods (Simple, kNN, Iterative/Multiple Imputation Concepts)
- Lesson 11: Encoding Categorical Features (One-Hot, Ordinal, Hashing, Target Encoding)
- Lesson 12: Train/Validation/Test Hygiene (Temporal Splits, Group Splits, Entity Leakage)
- Lesson 13: Building Preprocessing Pipelines (Fit/Transform Discipline, Column-Wise Pipelines)
- Lesson 1: Visualizing Data Distributions
- Lesson 2: Pairwise Relationships (Correlation, Scatterplots)
- Lesson 3: Detecting Patterns in Data
- Lesson 4: Dimensionality Reduction (Intro to PCA)
- Lesson 5: Using Tools like Pandas, Matplotlib, and Seaborn
- Lesson 6: Statistical Hypothesis Testing in ML Context
- Lesson 7: EDA for Data Quality (Duplicates, Inconsistencies, Drift, Label Issues)
- Lesson 8: Multicollinearity and Confounding Signals (Detection and Mitigation)
- Lesson 9: Leakage Forensics in EDA (Suspicious Features, Post-Outcome Variables)
- Lesson 10: EDA Reporting (Narratives, Assumptions, and Actionable Insights)
- Lesson 1: Introduction to Regression and Classification
- Lesson 2: Linear Regression: Concept and Applications
- Lesson 3: Logistic Regression: Binary Classification
- Lesson 4: Overfitting and Regularization (Ridge, LASSO, Elastic Net)
- Lesson 5: All Model Evaluation Metrics (MAE, MSE, RMSE, Accuracy, etc.)
- Lesson 6: Bias-Variance Tradeoff and Model Complexity
- Lesson 7: Polynomial and Interaction Terms in Regression Models
- Lesson 8: Generalized Linear Models (GLMs) Overview (Link Functions, Likelihood)
- Lesson 9: Multiclass Logistic Regression (Softmax) and Evaluation
- Lesson 10: Ordinal Regression (When Class Order Matters)
- Lesson 11: Robust Regression (Huber, RANSAC Concepts and Use Cases)
- Lesson 12: Quantile Regression and Prediction Intervals (Intro)
- Lesson 13: Cost-Sensitive Learning Basics (Thresholding, Costs, Utility)
- Lesson 1: Concept of Decision Trees
- Lesson 2: CART (Classification and Regression Trees)
- Lesson 3: Pruning and Overfitting in Trees
- Lesson 4: CHAID and M5 Model Trees
- Lesson 5: C4.5 and C5.0 Decision Trees
- Lesson 6: Interpretability and Feature Importance in Trees
- Lesson 7: Split Criteria (Gini, Entropy, Gain Ratio, Variance Reduction)
- Lesson 8: Handling Missing Values and Categorical Variables in Trees
- Lesson 9: Tree Stability, Variance, and Sensitivity Analysis
- Lesson 10: Monotonic Constraints and Business Rules in Tree Models (Concepts)
- Lesson 1: What is Ensemble Learning?
- Lesson 2: Bagging Algorithms (Random Forest, Bootstrap Aggregation)
- Lesson 3: Boosting Algorithms (AdaBoost, Gradient Boosting)
- Lesson 4: Stacking and Blending Techniques
- Lesson 5: Comparison of Ensemble Methods
- Lesson 6: Voting Classifiers and Averaging Methods
- Lesson 7: Bias-Variance Reduction via Ensembles
- Lesson 8: Out-of-Bag (OOB) Estimation and When It Works
- Lesson 9: Extremely Randomized Trees (ExtraTrees) and Diversity
- Lesson 10: Calibration with Ensembles (Platt Scaling, Isotonic Regression Overview)
- Lesson 11: Imbalanced Data with Ensembles (Class Weights, Balanced RF, Thresholding)
- Lesson 1: Concept of SVM for Classification
- Lesson 2: Kernel Functions in SVM
- Lesson 3: Soft Margin and Hyperparameters
- Lesson 4: Support Vector Regression (SVR)
- Lesson 5: Applications of SVM in Real-World Problems
- Lesson 6: Mathematical Formulation of the SVM Optimization Problem
- Lesson 7: Kernel Trick Intuition and Feature Spaces
- Lesson 8: Multi-Class SVM Strategies (OvR, OvO) and Practical Tradeoffs
- Lesson 9: SVM Probability Estimates and Calibration Considerations
- Lesson 10: Scaling SVMs (Approximate Kernels, Linear SVMs, Complexity)
- Lesson 1: K-Nearest Neighbors (KNN) Algorithm
- Lesson 2: Choosing the Right K
- Lesson 3: Distance Metrics and Weighting
- Lesson 4: Locally Weighted Learning (LWL)
- Lesson 5: Applications and Challenges
- Lesson 6: Curse of Dimensionality and Its Impact on KNN
- Lesson 7: kNN Regression and Local Smoothing Bias/Variance
- Lesson 8: Approximate Nearest Neighbors (KD-Trees, Ball Trees, ANN Concepts)
- Lesson 9: Metric Learning Overview (When Distances Should Be Learned)
- Lesson 1: Naïve Bayes Classifier
- Lesson 2: Gaussian Naïve Bayes
- Lesson 3: Bayesian Linear Regression
- Lesson 4: Assumptions and Limitations
- Lesson 5: Case Studies with Probabilistic Models
- Lesson 6: Maximum Likelihood vs. Bayesian Estimation
- Lesson 7: Bayesian Decision Theory (Risk, Loss, Bayes Optimal Classifier)
- Lesson 8: Priors and Conjugacy (Conceptual Toolkit for Fast Bayesian Updates)
- Lesson 9: MAP Estimation, Regularization Connections, and Interpretations
- Lesson 10: Expectation-Maximization (EM) Intuition (Preview for Later Chapters)
- Lesson 1: Introduction to Clustering
- Lesson 2: K-Means Clustering
- Lesson 3: Hierarchical Clustering
- Lesson 4: Gaussian Mixture Models (GMM)
- Lesson 5: Applications of Clustering Techniques
- Lesson 6: Evaluation Metrics for Clustering (Silhouette, Davies-Bouldin)
- Lesson 7: K-Means Variants (k-medoids, k-means++ initialization)
- Lesson 8: Model-Based Clustering and Selecting Number of Clusters (AIC/BIC concepts)
- Lesson 9: Constraints and Practicalities (Must-Link/Cannot-Link, Business Constraints)
- Lesson 10: Clustering at Scale (Mini-Batch K-Means and Sampling Strategies)
- Lesson 1: Principal Component Analysis (PCA)
- Lesson 2: t-SNE for Visualization
- Lesson 3: Linear Discriminant Analysis (LDA)
- Lesson 4: Feature Selection vs Feature Extraction
- Lesson 5: Applications of Dimensionality Reduction
- Lesson 6: Independent Component Analysis (ICA)
- Lesson 7: Random Projections and Johnson–Lindenstrauss Intuition
- Lesson 8: Non-negative Matrix Factorization (NMF) for Parts-Based Representations
- Lesson 9: Sparse PCA and Interpretability Tradeoffs
- Lesson 10: Embedded Feature Selection (L1, Tree-Based, Permutation Importance Overview)
- Lesson 1: Concept of Association Rules
- Lesson 2: Apriori Algorithm
- Lesson 3: Eclat Algorithm
- Lesson 4: Market Basket Analysis
- Lesson 5: Challenges and Limitations
- Lesson 6: Evaluation Metrics (Support, Confidence, Lift)
- Lesson 7: FP-Growth (Frequent Pattern Growth) and When It Outperforms Apriori
- Lesson 8: Rule Pruning and Redundancy Control
- Lesson 9: Sequential Pattern Mining (Concepts and Use Cases)
- Lesson 1: Train-Test Split and Validation
- Lesson 2: K-Fold Cross-Validation
- Lesson 3: Stratified Sampling in Cross-Validation
- Lesson 4: Performance Metrics for Classification
- Lesson 5: Performance Metrics for Regression
- Lesson 6: Model Calibration and ROC Curves
- Lesson 7: Learning Curves and Validation Curves
- Lesson 8: Nested Cross-Validation for Model Selection (Leakage Avoidance)
- Lesson 9: Bootstrap Methods for Performance Estimation and Uncertainty
- Lesson 10: Statistical Tests for Comparing Models (Paired Tests, Practical Significance)
- Lesson 11: Threshold Tuning and Decision Analysis (Costs, Utility, Decision Curves)
- Lesson 12: Confidence Intervals for Metrics and Reporting Standards
- Lesson 1: Introduction to Time Series Data
- Lesson 2: ARIMA Model
- Lesson 3: Decomposition and Seasonal Patterns
- Lesson 4: Dynamic Time Warping
- Lesson 5: Prophet for Time Series Forecasting
- Lesson 6: Feature Engineering for Time Series ML Models
- Lesson 7: Stationarity, ACF/PACF, and Differencing Practicalities
- Lesson 8: Time Series Cross-Validation (Rolling/Expanding Windows)
- Lesson 9: Forecast Accuracy Metrics (MAPE/sMAPE/MASE, Horizon-Based Evaluation)
- Lesson 10: Exogenous Variables and Feature Lags (ARIMAX/Regression with Lags Concepts)
- Lesson 11: Change Points and Regime Shifts (Introductory Concepts)
- Lesson 1: XGBoost: Concepts and Implementation
- Lesson 2: LightGBM and CatBoost
- Lesson 3: Advanced Hyperparameter Tuning in Boosting
- Lesson 4: Comparison of Gradient Boosting Variants
- Lesson 5: Applications and Limitations
- Lesson 6: Interpretability and SHAP Values in Boosting Models
- Lesson 7: Regularization and Constraints in Gradient Boosting (Shrinkage, Depth, Monotonicity)
- Lesson 8: Handling Categorical Variables and Missingness in Modern GBDTs (Conceptual + Practical)
- Lesson 9: Robustness, Leakage, and Validation Pitfalls in Boosted Models
- Lesson 1: Introduction to Semi-Supervised Learning
- Lesson 2: Self-Training and Co-Training Approaches
- Lesson 3: Active Learning for Data Labeling
- Lesson 4: Applications of Semi-Supervised Learning
- Lesson 5: Case Studies
- Lesson 6: Label Propagation and Graph-Based Semi-Supervised Learning
- Lesson 7: Query Strategies (Uncertainty Sampling, Diversity Sampling, Expected Model Change)
- Lesson 8: Evaluation Protocols for Active Learning (Budget Curves, Label Noise, Stopping Rules)
- Lesson 9: Weak Supervision and Silver Labels (Concepts, Risks, Governance)
- Lesson 1: Density-Based Clustering (DBSCAN)
- Lesson 2: Mean Shift Clustering
- Lesson 3: Spectral Clustering
- Lesson 4: Fuzzy C-Means Clustering
- Lesson 5: Comparison of Advanced Clustering Algorithms
- Lesson 6: Performance Metrics for Clustering
- Lesson 7: Cluster Stability and Validation Techniques
- Lesson 8: OPTICS and HDBSCAN (Variable Density Clustering Concepts)
- Lesson 9: Clustering High-Dimensional Data (Distance Concentration, Subspace Methods Overview)
- Lesson 10: Clustering Streams and Incremental Updates (Concepts)
- Lesson 1: Introduction to Bayesian Networks
- Lesson 2: Markov Random Fields
- Lesson 3: Conditional Random Fields
- Lesson 4: Applications in Real-World Problems
- Lesson 5: Challenges in Building Bayesian Models
- Lesson 6: Structure Learning and Inference Algorithms
- Lesson 7: Exact Inference (Variable Elimination) vs Approximate Inference (Sampling/Variational)
- Lesson 8: Belief Propagation and Message Passing Intuition
- Lesson 9: Parameter Learning with EM in Graphical Models
- Lesson 1: Long-Term Forecasting Techniques
- Lesson 2: Prophet in Depth
- Lesson 3: Regularization in Time Series
- Lesson 4: Dynamic Time Warping for Sequence Alignment
- Lesson 5: Use Cases in Finance and Healthcare
- Lesson 6: ML-Based Time Series Models (Random Forest, XGBoost, SVR)
- Lesson 7: State Space Models and Kalman Filtering (Conceptual + Practical)
- Lesson 8: Volatility and Heteroskedasticity (ARCH/GARCH Concepts)
- Lesson 9: Hierarchical and Grouped Forecasting (Reconciliation Concepts)
- Lesson 10: Conformal Prediction for Forecast Uncertainty (Intro)
- Lesson 11: Anomaly Detection and Event Detection in Time Series (Classical Methods)
- Lesson 1: Introduction to Transformer Models
- Lesson 2: Applications of Transformers in ML
- Lesson 3: Vision Transformers (ViT)
- Lesson 4: Comparing Transformers with Traditional Methods
- Lesson 5: Advanced Research Trends
- Lesson 6: Transfer Learning and Fine-Tuning Principles
- Lesson 7: Conceptual Positioning (Why Transformers Are Typically Considered Deep Learning)
- Lesson 8: Classical Alternatives for Sequences (HMMs, State Space, DTW, Feature-Based Models)
- Lesson 1: Advanced Regularization (Elastic Net, etc.)
- Lesson 2: L1 and L2 Regularization in Depth
- Lesson 3: Impact of Regularization on Overfitting
- Lesson 4: Applications in Sparse Data
- Lesson 5: Case Studies
- Lesson 6: Dropout and Noise-Based Regularization (General ML Perspective)
- Lesson 7: Regularization Paths and Model Selection (Lambda Grids, Warm Starts)
- Lesson 8: Group Lasso, Fused Lasso, and Structured Sparsity (Overview)
- Lesson 9: Regularization Beyond Linear Models (Trees/Boosting Constraints, Early Stopping)
- Lesson 1: Grid Search and Random Search
- Lesson 2: Bayesian Optimization
- Lesson 3: Genetic Algorithms for Optimization
- Lesson 4: Tuning Models with Optuna
- Lesson 5: Best Practices in Hyperparameter Tuning
- Lesson 6: Early Stopping and Learning Rate Scheduling
- Lesson 7: Multi-Fidelity Optimization (Successive Halving, Hyperband Concepts)
- Lesson 8: Parallel and Distributed HPO (Practical Patterns and Failure Modes)
- Lesson 9: Search Space Design (Priors, Conditional Spaces, Constraints, and Budgets)
- Lesson 1: Explainable AI (XAI)
- Lesson 2: Fairness and Bias in Machine Learning
- Lesson 3: Ethical Considerations in ML Models
- Lesson 4: Automated Machine Learning (AutoML)
- Lesson 5: Research Directions in ML
- Lesson 6: Model Compression and Distillation
- Lesson 7: Continual and Lifelong Learning in ML
- Lesson 8: Data-Centric AI (Systematic Data Improvement, Label Governance)
- Lesson 9: Conformal Prediction and Reliability Guarantees (Survey)
- Lesson 10: Privacy-Preserving ML (Differential Privacy Concepts)
- Lesson 11: Federated Learning (Non-Deep Learning Perspective, Constraints and Tradeoffs)
- Lesson 12: Robust ML and Distribution Shift (OOD, Stress Testing, Robustness Curves)
- Lesson 13: Adversarial ML for Classical Models (Poisoning, Evasion, Threat Modeling)
- Lesson 1: Optimization Problem Setup (Objectives, Constraints, Regularizers)
- Lesson 2: Convexity, Strong Convexity, and Why They Matter
- Lesson 3: Gradient Descent, SGD, Momentum (Classical View)
- Lesson 4: Newton, Quasi-Newton (BFGS/L-BFGS) and Second-Order Intuition
- Lesson 5: Coordinate Descent and Proximal Methods (L1/Lasso Context)
- Lesson 6: Duality and KKT Conditions (SVM/Regularization Connections)
- Lesson 7: Numerical Stability, Conditioning, and Practical Debugging
- Lesson 1: Generalization Error and Concentration Intuition
- Lesson 2: VC Dimension (Conceptual) and Capacity Measures
- Lesson 3: PAC Learning (High-Level) and Sample Complexity
- Lesson 4: Regularization as Capacity Control
- Lesson 5: Bias-Variance Revisited Through Theory
- Lesson 6: No Free Lunch Theorems and Practical Implications
- Lesson 1: Model Selection vs Hyperparameter Tuning vs Statistical Inference
- Lesson 2: Filter Methods (Mutual Information, Chi-Square, ANOVA F-test)
- Lesson 3: Wrapper Methods (RFE, Forward/Backward Selection) and Costs
- Lesson 4: Embedded Methods (L1, Trees, Stability Selection Concepts)
- Lesson 5: Permutation Importance and Pitfalls (Correlation, Leakage, Variance)
- Lesson 6: Multiple Comparisons and Selection Bias (Why Results Look Too Good)
- Lesson 7: Parsimony, Interpretability, and Governance Requirements
- Lesson 1: Kernels as Similarity Functions (Design and Validity)
- Lesson 2: Kernel Ridge Regression and Regularization
- Lesson 3: Kernel PCA and Nonlinear Manifolds
- Lesson 4: Approximate Kernels (Nyström, Random Fourier Features)
- Lesson 5: Choosing Kernels and Tuning in Practice
- Lesson 6: When Kernel Methods Beat Trees (and When They Don’t)
- Lesson 1: GP Intuition (Distributions over Functions)
- Lesson 2: Covariance Functions (RBF, Matérn, Periodic) and Prior Beliefs
- Lesson 3: Hyperparameter Learning (Marginal Likelihood)
- Lesson 4: Uncertainty Quantification and Prediction Intervals
- Lesson 5: Scalability (Sparse GPs, Inducing Points Concepts)
- Lesson 6: GP Classification (Laplace/Variational Ideas at a High Level)
- Lesson 7: Practical Use Cases and Limitations
- Lesson 1: Problem Definitions (Outliers vs Anomalies vs Novelty)
- Lesson 2: Statistical Methods (Z-score, Robust Covariance, MAD)
- Lesson 3: Density-Based Methods (GMM-Based, KDE Concepts)
- Lesson 4: Isolation Forest and Tree-Based Anomaly Detection
- Lesson 5: One-Class SVM and Support Estimation
- Lesson 6: Local Outlier Factor (LOF) and Neighborhood Methods
- Lesson 7: Evaluation Without Labels (Proxy Metrics, Triage Workflows)
- Lesson 1: Recommendation Problem Types (Explicit vs Implicit Feedback)
- Lesson 2: Baselines and Heuristics (Popularity, Co-Occurrence, Rules)
- Lesson 3: Collaborative Filtering (User–User, Item–Item)
- Lesson 4: Matrix Factorization (SVD/ALS Concepts)
- Lesson 5: Content-Based Recommendation (Feature Engineering)
- Lesson 6: Ranking Metrics (NDCG, MAP, Recall@K) and Offline Evaluation
- Lesson 7: Cold Start, Leakage, and Serving-Time Constraints
- Lesson 1: Correlation vs Causation and Why Predictive Models Mislead
- Lesson 2: Potential Outcomes Framework (ATE, ATT Concepts)
- Lesson 3: Confounding, Selection Bias, and Backdoor Intuition
- Lesson 4: Propensity Scores (Matching, Weighting, Stratification)
- Lesson 5: Doubly Robust Estimation (High-Level) and Practical Guardrails
- Lesson 6: Uplift Modeling and Treatment Effect Heterogeneity (Conceptual)
- Lesson 7: A/B Testing Design, Power, and Common Failure Modes
- Lesson 1: Aleatoric vs Epistemic Uncertainty (Practical Meaning)
- Lesson 2: Calibration in Classification (Reliability Diagrams, ECE Concepts)
- Lesson 3: Prediction Intervals in Regression (Quantile Regression Linkage)
- Lesson 4: Conformal Prediction for Regression and Classification
- Lesson 5: Distribution Shift and Validity Degradation
- Lesson 6: Risk-Based Decisioning and Human-in-the-Loop Thresholds
- Lesson 1: Dataset Bias and Measurement Bias (Taxonomy and Examples)
- Lesson 2: Fairness Metrics (Demographic Parity, Equalized Odds, Calibration Tradeoffs)
- Lesson 3: Bias Mitigation (Pre-, In-, Post-Processing Approaches)
- Lesson 4: Privacy Concepts (PII, Re-Identification Risk, Data Minimization)
- Lesson 5: Differential Privacy Basics (Noise Mechanisms and Utility Tradeoffs)
- Lesson 6: Threat Modeling for ML Systems (Poisoning, Evasion, Model Extraction)
- Lesson 7: Secure Evaluation and Red-Teaming ML Pipelines (Process)
- Lesson 1: Reproducible Pipelines (Data/Code/Model Versioning Concepts)
- Lesson 2: Feature Stores and Training/Serving Consistency
- Lesson 3: Deployment Patterns (Batch, Online, Streaming) for Classical Models
- Lesson 4: Monitoring (Data Drift, Concept Drift, Performance Decay)
- Lesson 5: Model Governance (Approvals, Documentation, Audit Trails)
- Lesson 6: Model Maintenance (Retraining Triggers, Backtesting, Rollbacks)
- Lesson 7: Experiment Tracking and Artifact Management (Practical Standards)
- Lesson 1: Computational Complexity in Training and Inference
- Lesson 2: Sampling, Sketching, and Approximation Concepts
- Lesson 3: Incremental/Online Learning (SGD Classifiers, Passive-Aggressive Concepts)
- Lesson 4: Streaming Feature Engineering and Windowing
- Lesson 5: Distributed Training for Classical Models (MapReduce/Spark Concepts)
- Lesson 6: Practical Performance Engineering (Vectorization, Caching, Memory Layout)
- Lesson 1: Survival Analysis (Censoring, Kaplan–Meier, Cox Model Concepts)
- Lesson 2: Count Models (Poisson/Negative Binomial Regression Use Cases)
- Lesson 3: Multi-Label Classification (Problem Transforms, Metrics)
- Lesson 4: Imbalanced and Rare-Event Modeling (PR Curves, Focal Costs, Evaluation)
- Lesson 5: Spatial and Geospatial Modeling Basics (Leakage, Autocorrelation Concepts)
- Lesson 6: Structured Data with Groups (Hierarchical Models Concepts, Group CV)
- Lesson 1: End-to-End Tabular Classification Project (From EDA to Monitoring Plan)
- Lesson 2: End-to-End Regression Project with Uncertainty (Intervals + Conformal)
- Lesson 3: Clustering and Segmentation Project (Stability + Business Interpretability)
- Lesson 4: Time Series Forecasting Project (Rolling CV + Drift Handling)
- Lesson 5: Anomaly Detection Project (Triage Workflow + Evaluation Without Labels)
- Lesson 6: Causal/Uplift Mini-Project (Policy/Intervention Decisioning)
- Lesson 7: Model Risk Review (Bias/Fairness/Privacy/Security Checklist + Documentation)
Install Python 3.9+ or a newer Python 3.x release.
Recommended options:
- Python from python.org
- Anaconda or Miniconda for data science workflows
python -m pip install --upgrade pip
python -m pip install notebook jupyterlabThen launch:
jupyter lab
# or
jupyter notebookconda install -c conda-forge notebook jupyterlabThen launch:
jupyter lab
# or
jupyter notebookFrom the repository root, create and activate a virtual environment:
python -m venv .venvWindows PowerShell:
.venv\Scripts\Activate.ps1Windows CMD:
.venv\Scripts\activate.batmacOS / Linux:
source .venv/bin/activateThen install the required packages:
python -m pip install --upgrade pip
python -m pip install -r requirement.txt- Open the repository page on GitHub.
- Click Code → Download ZIP.
- Extract the ZIP file to a local folder.
git clone https://github.com/mohammadijoo/Machine_Learning_Tutorials.git
cd Machine_Learning_TutorialsTo ensure Jupyter shows only the files and folders inside this repository, start Jupyter from the repository root directory.
cd Machine_Learning_Tutorials
jupyter labcd Machine_Learning_Tutorials
jupyter notebookOptional explicit notebook root:
cd Machine_Learning_Tutorials
jupyter notebook --notebook-dir="."From the file browser, navigate to:
Tutorials/English/ChapterX/for English notebooksTutorials/Persian/ChapterX/for Persian notebooks ending in_Fa.ipynb
In each notebook, the first cell is responsible for applying display styling, including CSS for right-to-left layout and Persian typography.
Before reading or executing the notebook:
- Open the notebook.
- Run the first cell.
- Continue with the remaining lesson cells.
This ensures that Persian notebooks render correctly and consistently.
For a structured path through the repository:
- Start with Chapters 1–4 to understand the ML workflow, data preparation, EDA, and supervised learning foundations.
- Continue with Chapters 5–14 for trees, ensembles, SVMs, probabilistic models, unsupervised learning, model evaluation, and time series.
- Study Chapters 15–23 for advanced model families, active learning, graphical models, regularization, HPO, XAI, fairness, and emerging topics.
- Use Chapters 24–36 to strengthen mathematical, statistical, reliability, causal, MLOps, scalability, and specialized classical ML knowledge.
- Finish with Chapter 37 capstone projects to practice complete end-to-end machine learning workflows.
A short video walkthrough shows how to download/clone the repo and run the notebooks.
This project is released under the MIT License. See the LICENSE file for details.