Introduction to Machine Learning with Python: A Hands-On Guide to Implementing Machine Learning Algorithms - Tapa blanda

Sinopsis

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.

• 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.

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