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
