Unlock the Power of Machine Learning—With Real Python Code
Want to understand how machines learn from data—and how to build your own intelligent systems?
Introduction to Machine Learning with Python is your practical, beginner-friendly path to mastering machine learning concepts and bringing them to life using Python.
Designed for programmers, data analysts, and aspiring ML engineers, this hands-on guide demystifies core techniques and walks you through implementing them step by step—no advanced math required.
What You'll Learn:
Machine learning fundamentals explained in plain English
How to prepare, clean, and split data for training and testing
Supervised learning: linear regression, decision trees, k-NN, SVM
Unsupervised learning: clustering, dimensionality reduction
Introduction to neural networks and deep learning
How to evaluate model performance with accuracy, precision, recall
Real-world projects using scikit-learn, NumPy, pandas, and matplotlib
Practical tips for tuning models and avoiding overfitting
A workflow you can follow to build your own ML systems
Packed with examples, visuals, and coding exercises, this guide gives you the skills to apply machine learning in real projects—from recommendations to predictions.
If you're ready to build intelligent systems with Python, this is the book to start with.