Machine Learning Algorithms

Complete Machine Learning Algorithms Reference Guide

With Detailed Formula Explanations

This book is a comprehensive, mathematically grounded reference to machine learning algorithms, designed for practitioners, students, and researchers who want more than surface-level explanations.

Unlike introductory tutorials that focus only on APIs, this guide dives deep into how algorithms actually work, explaining the mathematics, assumptions, trade-offs, and decision logic behind each method — while remaining practical and implementation-oriented.

What makes this book different?

This is not just a catalog of algorithms. It is a decision framework for real-world machine learning:

• How do you choose the right algorithm for a given problem?
• What data characteristics matter most?
• Which assumptions, hyperparameters, and pitfalls can silently break your model?
• How do you move from theory to production-ready models?

The book answers these questions with structured checklists, decision trees, worked examples, and detailed formula walkthroughs.

What you’ll learn

• Algorithm selection methodology
  Step-by-step frameworks, decision trees, and checklists for classification, regression, clustering, time series, dimensionality reduction, and imbalanced data problems.
• Deep mathematical explanations
  Clear derivations and interpretations of formulas behind:
• • Linear and logistic regression
  • Decision trees, random forests, boosting methods
  • Support Vector Machines (linear, kernel, ν-SVM)
  • Probabilistic models (Naive Bayes, Bayesian networks)
  • Clustering methods (K-Means, DBSCAN, GMM, hierarchical, spectral, fuzzy)
  • Neural networks, CNNs, LSTMs, and backpropagation
• Practical implementation guidance
  Hyperparameter ranges, scaling requirements, validation strategies, common error messages, and production pitfalls.
• Worked, end-to-end examples
  Realistic case studies such as spam detection, customer segmentation, medical diagnosis, dimensionality reduction, and model selection — including full metric analysis and common mistakes.
• Model evaluation, interpretability, and deployment
  Coverage of cross-validation, bias–variance tradeoff, SHAP, LIME, feature importance, monitoring in production, and maintenance strategies.

Who this book is for

• Machine learning practitioners who want a reliable reference they can consult daily
• Data scientists and engineers who need help choosing and justifying models
• Students who want rigorous mathematical explanations without losing practical intuition
• Researchers looking for a structured overview of classical and modern ML methods

How to use this book

You can read it sequentially, but it’s also designed to work as a lookup and decision guide:

• Jump directly to an algorithm
• Use checklists to validate your approach
• Compare methods using complexity and suitability tables
• Revisit worked examples when debugging real projects

Complete Machine Learning Algorithms Reference Guide

With Detailed Formula Explanations

This book is a comprehensive, mathematically grounded reference to machine learning algorithms, designed for practitioners, students, and researchers who want more than surface-level explanations.

Unlike introductory tutorials that focus only on APIs, this guide dives deep into how algorithms actually work, explaining the mathematics, assumptions, trade-offs, and decision logic behind each method — while remaining practical and implementation-oriented.

What makes this book different?

This is not just a catalog of algorithms. It is a decision framework for real-world machine learning:

• How do you choose the right algorithm for a given problem?
• What data characteristics matter most?
• Which assumptions, hyperparameters, and pitfalls can silently break your model?
• How do you move from theory to production-ready models?

The book answers these questions with structured checklists, decision trees, worked examples, and detailed formula walkthroughs.

What you’ll learn

• Algorithm selection methodology
  Step-by-step frameworks, decision trees, and checklists for classification, regression, clustering, time series, dimensionality reduction, and imbalanced data problems.
• Deep mathematical explanations
  Clear derivations and interpretations of formulas behind:
• • Linear and logistic regression
  • Decision trees, random forests, boosting methods
  • Support Vector Machines (linear, kernel, ν-SVM)
  • Probabilistic models (Naive Bayes, Bayesian networks)
  • Clustering methods (K-Means, DBSCAN, GMM, hierarchical, spectral, fuzzy)
  • Neural networks, CNNs, LSTMs, and backpropagation
• Practical implementation guidance
  Hyperparameter ranges, scaling requirements, validation strategies, common error messages, and production pitfalls.
• Worked, end-to-end examples
  Realistic case studies such as spam detection, customer segmentation, medical diagnosis, dimensionality reduction, and model selection — including full metric analysis and common mistakes.
• Model evaluation, interpretability, and deployment
  Coverage of cross-validation, bias–variance tradeoff, SHAP, LIME, feature importance, monitoring in production, and maintenance strategies.

Who this book is for

• Machine learning practitioners who want a reliable reference they can consult daily
• Data scientists and engineers who need help choosing and justifying models
• Students who want rigorous mathematical explanations without losing practical intuition
• Researchers looking for a structured overview of classical and modern ML methods

How to use this book

You can read it sequentially, but it’s also designed to work as a lookup and decision guide:

• Jump directly to an algorithm
• Use checklists to validate your approach
• Compare methods using complexity and suitability tables
• Revisit worked examples when debugging real projects

Learn ML Algorithms by Doing

This comprehensive exercise book transforms machine learning theory into practice. Rather than passively reading about algorithms, you'll work through carefully designed problems that build understanding from first principles.

What You'll Get

Worked Examples Every section begins with fully solved problems that walk you through algorithm mechanics step-by-step. See exactly how to:

• Calculate probabilities with logistic regression
• Build decision trees using information gain
• Perform K-means clustering by hand
• Propagate errors through neural networks
• And much more...

Progressive Practice Exercises Problems are organized by difficulty:

• ⋆ Basic: Test fundamental understanding
• ⋆⋆ Intermediate: Combine multiple concepts
• ⋆⋆⋆ Advanced: Deep dive into algorithm internals

Real-World Applications Exercises drawn from actual use cases:

• Medical diagnosis systems with asymmetric costs
• Feature selection for high-dimensional data
• Imbalanced classification in practice
• Model deployment considerations
• Cross-validation strategies

Mathematical Rigor Detailed derivations and formula explanations ensure you understand the "why" behind each algorithm, not just the "how."

Topics Covered

• Classification: Logistic regression, decision trees, random forests, SVMs
• Regression: Linear regression, ridge/lasso regularization, polynomial fitting, evaluation metrics
• Clustering: K-means, hierarchical clustering
• Dimensionality Reduction: PCA, t-SNE, UMAP, feature selection
• Evaluation: Classification metrics, ROC curves, cost-sensitive learning
• Neural Networks: Forward propagation, backpropagation, activation functions
• Advanced Topics: Ensemble methods, imbalanced data handling, cross-validation, model deployment

Who Should Read This?

• Students learning machine learning fundamentals
• Data Scientists looking to deepen algorithm understanding
• Practitioners preparing for technical interviews
• Researchers needing mathematical details and implementation insights
• Self-taught developers wanting to master core concepts

How to Use This Book

1. Start with Worked Examples: Fully solved problems show you the process
2. Attempt Practice Exercises: Try solving independently before checking answers
3. Use Hints Strategically: Get unstuck without spoiling the learning
4. Check Understanding: Detailed explanations ensure comprehension
5. Progress Gradually: Build confidence through increasing difficulty

Companion to the Complete Guide

This exercise book is designed as a practical complement to the Complete Machine Learning Algorithms Reference Guide. Together, they provide:

• Theoretical foundations in the reference guide
• Hands-on practice through worked examples and exercises

However, each book can be used independently—this exercise book contains all the context needed to solve problems successfully.

What Makes This Different?

• Author-created: Personally structured outline, verified technical accuracy, edited for clarity
• Mathematically detailed: Full derivations, not just formulas
• Pedagogically sound: Progressive difficulty, hints without spoilers, multiple solution approaches
• Practical focus: Real-world context, hyperparameter guidance, implementation tips
• Comprehensive coverage: 9 chapters spanning classical to modern algorithms

Inside You'll Find

• 30+ worked examples with complete solutions
• 100+ practice problems across all difficulty levels
• Step-by-step derivations (including gradient descent, backpropagation, and more)
• Confusion matrices, ROC curves, and evaluation metrics worked out by hand
• Cost-sensitive thresholds and optimal decision-making
• Feature selection comparison with real datasets
• Neural network forward and backward passes traced completely
• Ensemble methods and stacking implementations

Learning Outcomes

After working through this book, you'll be able to:

• Calculate algorithm outputs by hand (proving deep understanding)
• Know when to apply each algorithm and why
• Tune hyperparameters intelligently
• Handle imbalanced data and asymmetric costs
• Evaluate models using appropriate metrics
• Deploy models considering practical constraints
• Derive algorithm updates from first principles
• Combine multiple algorithms effectively

Format

This is a living book on Leanpub—updated regularly as new content is added and based on reader feedback. Your purchase includes all future updates at no additional cost.

Start Your ML Mastery Journey Today

Don't just memorize algorithms—truly understand them. Grab your copy and start working through problems that build real expertise.

Companion to: Complete Machine Learning Algorithms Reference Guide

Updated: February 2026

Complete Machine Learning Algorithms Reference Guide

With Detailed Formula Explanations

This book is a comprehensive, mathematically grounded reference to machine learning algorithms, designed for practitioners, students, and researchers who want more than surface-level explanations.

Unlike introductory tutorials that focus only on APIs, this guide dives deep into how algorithms actually work, explaining the mathematics, assumptions, trade-offs, and decision logic behind each method — while remaining practical and implementation-oriented.

What makes this book different?

This is not just a catalog of algorithms. It is a decision framework for real-world machine learning:

• How do you choose the right algorithm for a given problem?
• What data characteristics matter most?
• Which assumptions, hyperparameters, and pitfalls can silently break your model?
• How do you move from theory to production-ready models?

The book answers these questions with structured checklists, decision trees, worked examples, and detailed formula walkthroughs.

What you’ll learn

• Algorithm selection methodology
  Step-by-step frameworks, decision trees, and checklists for classification, regression, clustering, time series, dimensionality reduction, and imbalanced data problems.
• Deep mathematical explanations
  Clear derivations and interpretations of formulas behind:
• • Linear and logistic regression
  • Decision trees, random forests, boosting methods
  • Support Vector Machines (linear, kernel, ν-SVM)
  • Probabilistic models (Naive Bayes, Bayesian networks)
  • Clustering methods (K-Means, DBSCAN, GMM, hierarchical, spectral, fuzzy)
  • Neural networks, CNNs, LSTMs, and backpropagation
• Practical implementation guidance
  Hyperparameter ranges, scaling requirements, validation strategies, common error messages, and production pitfalls.
• Worked, end-to-end examples
  Realistic case studies such as spam detection, customer segmentation, medical diagnosis, dimensionality reduction, and model selection — including full metric analysis and common mistakes.
• Model evaluation, interpretability, and deployment
  Coverage of cross-validation, bias–variance tradeoff, SHAP, LIME, feature importance, monitoring in production, and maintenance strategies.

Who this book is for

• Machine learning practitioners who want a reliable reference they can consult daily
• Data scientists and engineers who need help choosing and justifying models
• Students who want rigorous mathematical explanations without losing practical intuition
• Researchers looking for a structured overview of classical and modern ML methods

How to use this book

You can read it sequentially, but it’s also designed to work as a lookup and decision guide:

• Jump directly to an algorithm
• Use checklists to validate your approach
• Compare methods using complexity and suitability tables
• Revisit worked examples when debugging real projects