Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition

Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition
Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition by Giuseppe Ciaburro
English | 2019 | ISBN: 1789808452 | 642 Pages | EPUB | 249 MB

Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch
This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.
With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.
By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
What you will learn

  • Use predictive modeling and apply it to real-world problems
  • Explore data visualization techniques to interact with your data
  • Learn how to build a recommendation engine
  • Understand how to interact with text data and build models to analyze it
  • Work with speech data and recognize spoken words using Hidden Markov Models
  • Get well versed with reinforcement learning, automated ML, and transfer learning
  • Work with image data and build systems for image recognition and biometric face recognition
  • Use deep neural networks to build an optical character recognition system