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Machine Learning & AI

Move from preprocessing and classical machine learning to model evaluation, deep learning, and GenAI foundations.

Foundations to Intermediate10 modules48 lessons published
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ROADMAP

Move through the path, one module at a time.

01

ML Fundamentals

Understand learning paradigms and the ML workflow.

7 published
  1. 01What Machine Learning Is and How It Differs from Traditional ProgrammingBeginner - 14 min readRead →
  2. 02Types of Machine LearningBeginner - 14 min readRead →
  3. 03Features, Targets, and SamplesBeginner - 15 min readRead →
  4. 04Your First Machine Learning Model with scikit-learnBeginner - 18 min readRead →
  5. 05Training, Inference, and the Machine Learning WorkflowBeginner - 15 min readRead →
  6. 06Train, Validation, Test Sets, and GeneralizationBeginner - 16 min readRead →
  7. 07Overfitting, Underfitting, and Bias-Variance IntuitionBeginner - 16 min readRead →
02

Regression

Model continuous outcomes and interpret predictions.

5 published
  1. 01Understanding Regression ProblemsBeginner - 15 min readRead →
  2. 02Simple Linear RegressionBeginner - 16 min readRead →
  3. 03Predictions, Residuals, and Regression LossBeginner - 16 min readRead →
  4. 04Multiple Linear Regression and CoefficientsIntermediate - 17 min readRead →
  5. 05Extending and Controlling Linear ModelsIntermediate - 17 min readRead →
03

Classification

Build classifiers for real decision problems.

5 published
  1. 01Understanding Classification ProblemsBeginner - 15 min readRead →
  2. 02Logistic Regression IntuitionBeginner - 16 min readRead →
  3. 03Probabilities, Thresholds, and Decision BoundariesBeginner - 16 min readRead →
  4. 04Binary and Multiclass ClassificationBeginner - 15 min readRead →
  5. 05Class Imbalance FundamentalsBeginner - 16 min readRead →
04

Model Evaluation

Choose metrics that reflect the real business goal.

7 published
  1. 01Why Baseline Models MatterBeginner - 15 min readRead →
  2. 02Regression Metrics for Real DecisionsBeginner - 16 min readRead →
  3. 03Reading a Confusion MatrixBeginner - 15 min readRead →
  4. 04Why Accuracy Is Not EnoughBeginner - 10 min readRead →
  5. 05ROC-AUC and Threshold Trade-offsIntermediate - 16 min readRead →
  6. 06Cross-Validation and Comparing ModelsIntermediate - 16 min readRead →
  7. 07Hyperparameter Tuning Without OverfittingIntermediate - 16 min readRead →
05

Data Preprocessing

Prepare real-world data for reliable modeling.

3 published
  1. 01Preparing Missing and Categorical Data for ModelsBeginner - 16 min readRead →
  2. 02Feature Scaling and TransformationsBeginner - 16 min readRead →
  3. 03Pipelines and Leakage-Safe PreprocessingIntermediate - 18 min readRead →
06

Feature Engineering

Create stronger signals from raw data.

4 published
  1. 01Creating and Selecting Useful FeaturesIntermediate - 16 min readRead →
  2. 02Interaction, Ratio, and Aggregation FeaturesIntermediate - 16 min readRead →
  3. 03Time-Based and Domain-Driven FeaturesIntermediate - 16 min readRead →
  4. 04Validating Features for Leakage, Stability, and AvailabilityIntermediate - 17 min readRead →
07

Trees & Ensembles

Build stronger nonlinear models with trees, bagging, and boosting.

4 published
  1. 01Decision Trees: Splits, Predictions, and OverfittingBeginner - 16 min readRead →
  2. 02Random Forests and BaggingBeginner - 16 min readRead →
  3. 03Boosting and Gradient Boosting IntuitionIntermediate - 16 min readRead →
  4. 04Comparing Ensembles and Feature Importance CarefullyIntermediate - 17 min readRead →
08

Unsupervised Learning

Find structure without labeled outcomes.

4 published
  1. 01What Is Unsupervised Learning?Beginner - 14 min readRead →
  2. 02Clustering with K-Means and Choosing KIntermediate - 16 min readRead →
  3. 03Hierarchical ClusteringIntermediate - 15 min readRead →
  4. 04Dimensionality Reduction and PCA IntuitionIntermediate - 16 min readRead →
09

Intro to Deep Learning

Understand neural networks without losing the fundamentals.

4 published
  1. 01AI vs Machine Learning vs Deep LearningBeginner - 14 min readRead →
  2. 02Neural Networks and Deep Learning IntuitionIntermediate - 17 min readRead →
  3. 03NLP and Computer Vision: What AI Models SolveIntermediate - 16 min readRead →
  4. 04Classical ML versus Deep Learning: Choosing the Right ToolIntermediate - 16 min readRead →
10

Intro to Generative AI

Learn the core ideas behind modern language models and RAG.

5 published
  1. 01Generative AI Foundations and Use CasesBeginner - 15 min readRead →
  2. 02Embeddings and Semantic SimilarityIntermediate - 16 min readRead →
  3. 03Transformers and LLM IntuitionIntermediate - 17 min readRead →
  4. 04Retrieval-Augmented Generation: RAG Concepts and BoundariesIntermediate - 25 min readRead →
  5. 05Responsible Generative AI Evaluation and LimitationsIntermediate - 18 min readRead →