(Paperback)
Miguel Farmer, Rafael Sanders, Boozman Richard,
Independently Published (Publisher)
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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.
