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Machine Learning Cover

Classical MLBeginner

Machine Learning

7 Chapters32 Lessons
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The course covers core machine learning topics at an introductory level: learning paradigms (supervised, unsupervised, reinforcement learning), data representation and feature engineering, classical algorithms (linear regression, decision trees, k-NN, SVM, naive Bayes), model evaluation (metrics, cross-validation, overfitting/underfitting), and the fundamentals of the scikit-learn library. ML technology currently underpins recommendation systems, medical diagnostics, natural language processing, and autonomous vehicles โ€” understanding its foundations is a competency required in a growing number of engineering and analytical roles. Prerequisites: basic Python proficiency (variables, loops, functions, lists) and elementary linear algebra (vectors, matrices). The course does not cover deep learning (neural networks, CNNs, transformers), production deployments, or MLOps. Upon completion, students understand the operating principles of major ML algorithms, can prepare data, train a model, and evaluate its quality using scikit-learn, and can consciously select an algorithm for a given problem class.

Chapters

MODULE 01

What is ML and the Mental Model

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Introduction to machine learning: definition, three paradigms (supervised, unsupervised, reinforcement), the full ML project workflow, and the train/validation/test data split.

  1. 1.1Definition and applications of ML
  2. 1.2Three learning paradigms: supervised, unsupervised, reinforcement
  3. 1.3The ML project workflow โ€” from problem to deployment
  4. 1.4Train / Validation / Test split โ€” the foundation of honest evaluation
MODULE 02

Data and Preparation

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Exploring, cleaning, and transforming data for ML models: EDA, numerical feature scaling, categorical encoding, handling missing values, and feature engineering.

  1. 2.1Exploratory Data Analysis (EDA) โ€” first contact with the dataset
  2. 2.2Numerical features and normalization
  3. 2.3Categorical features and encoding
  4. 2.4Missing data and cleaning
  5. 2.5Feature engineering
MODULE 03

Regression

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Linear and polynomial regression: intuition, MSE cost function, gradient descent, hyperparameters, evaluation metrics, and hands-on scikit-learn practice.

  1. 3.1Linear regression โ€” intuition
  2. 3.2MSE cost function
  3. 3.3Gradient descent
  4. 3.4Polynomial regression and hyperparameters
  5. 3.5Metrics and scikit-learn in practice
MODULE 04

Classification

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Binary and multiclass classification in machine learning: logistic regression, decision trees, k-NN, naive Bayes, quality metrics, and model diagnostics with confusion matrix and ROC curve.

  1. 4.1Logistic regression โ€” the foundation of classification
  2. 4.2Decision trees โ€” splits, Gini, and pruning
  3. 4.3k-NN and Naive Bayes โ€” classification by similarity and probability
  4. 4.4Classification metrics โ€” accuracy, precision, recall, F1
  5. 4.5Confusion matrix and ROC curve
MODULE 05

Overfitting, Underfitting, and Regularization

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Diagnosing and controlling model capacity in machine learning: underfitting and overfitting, the bias-variance tradeoff, L1 (Lasso) and L2 (Ridge) regularization, and cross-validation as a tool for model selection and generalization error estimation.

  1. 5.1Underfitting and overfitting โ€” diagnosing model fit
  2. 5.2Bias-variance tradeoff โ€” the formal anatomy of error
  3. 5.3L1 and L2 regularization โ€” Lasso, Ridge, and Elastic Net
  4. 5.4Cross-validation โ€” k-fold, stratified, nested, and practical pitfalls
MODULE 06

Ensembles and Model Selection

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Ensemble methods and systematic model selection in machine learning: the intuition behind combining multiple predictors (bagging, boosting, stacking), Random Forest, gradient boosting and XGBoost, model interpretation via feature importance, and organizing the full workflow in a scikit-learn pipeline with cross-validation and hyperparameter search.

  1. 6.1Intuition behind ensembles โ€” why many models beat one
  2. 6.2Random Forest โ€” bagging trees with feature subsampling
  3. 6.3Gradient Boosting and XGBoost โ€” sequential error correction
  4. 6.4Feature importance โ€” interpreting ensemble models
  5. 6.5Scikit-learn pipeline and model selection
MODULE 07

Unsupervised Learning

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Label-free learning algorithms โ€” from k-means grouping through hierarchical dendrograms to PCA dimensionality reduction and their practical limitations.

  1. 7.1k-means clustering
  2. 7.2Hierarchical clustering
  3. 7.3PCA โ€” dimensionality reduction
  4. 7.4Applications and limitations