Machine Learning for Absolute Beginners

You don't need a math degree to build a machine learning model that actually works.

You need a clear path — and that's exactly what this book gives you.

Machine Learning for Absolute Beginners is Book 1 of the Data Science Foundations Series, a hands-on, no-fluff introduction to machine learning with Python and scikit-learn. Every concept is taught through real, runnable code — not abstract theory you'll forget by next chapter.

Inside, you'll learn how to:

• Build and evaluate real models with scikit-learn: linear regression, logistic regression, decision trees, random forests, and k-NN
• Avoid the #1 beginner mistake — data leakage — and split your data the right way
• Master the full ML workflow: cleaning data, feature engineering, train/test splits, and cross-validation
• Tune hyperparameters with GridSearchCV and RandomizedSearchCV like a working data scientist
• Cluster unlabeled data with K-Means and know exactly when (and why) it fails
• Build a complete, deployable model from messy, real-world data — start to finish

Why this book is different:

Every chapter ends with hands-on exercises and fully worked solutions. Every claim is backed by actual code output — real numbers, real charts, real mistakes shown and corrected, not hidden. The final chapter walks you through one complete, end-to-end machine learning project, tying together everything from Chapter 1 to Chapter 15 into a single deployable pipeline.

Whether you're a complete programming beginner, a Python user looking to break into data science, or a student preparing for a career in AI, this book gives you the practical machine learning foundation that everything else — deep learning, AI tools, statistics — builds on.

No prior experience with machine learning, statistics, or advanced math required. Just Python basics and a genuine willingness to type the code yourself.

Datasets used throughout this book are available for free download at www.waskeypress.com.