[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","2026-02-15T20:00:12.442Z","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","system","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzU4NjY","title","Machine Learning Algorithms Exercise Book","state","published","contactAuthorMethod","email","aboutText","

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

Start with Worked Examples: Fully solved problems show you the process
Attempt Practice Exercises: Try solving independently before checking answers
Use Hints Strategically: Get unstuck without spoiling the learning
Check Understanding: Detailed explanations ensure comprehension
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

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