Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI).
With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance.
By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

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Spis treści

Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition eBook -- spis treści

• 1. Getting Started with Python Machine Learning
• 2. Classifying with Real-world Examples
• 3. Regression
• 4. Classification I – Detecting Poor Answers
• 5. Dimensionality Reduction
• 6. Clustering – Finding Related Posts
• 7. Recommendations
• 8. Artificial neural Networks & Deep Learning
• 9. Classification II – Sentiment Analysis
• 10. Topic Modeling
• 11. Classification III – Music Genre Classification
• 12. Computer Vision
• 13. Reinforcement Learning
• 14. Bigger Data