E-book details

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition

Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition

Sebastian Raschka, Vahid Mirjalili

Ebook
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
  • 1. Giving Computers the Ability to Learn from Data
  • 2. Training Simple Machine Learning Algorithms for Classification
  • 3. A Tour of Machine Learning Classifiers Using scikit-learn
  • 4. Building Good Training Datasets – Data Preprocessing
  • 5. Compressing Data via Dimensionality Reduction
  • 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
  • 7. Combining Different Models for Ensemble Learning
  • 8. Applying Machine Learning to Sentiment Analysis
  • 9. Embedding a Machine Learning Model into a Web Application
  • 10. Predicting Continuous Target Variables with Regression Analysis
  • 11. Working with Unlabeled Data – Clustering Analysis
  • 12. Implementing a Multilayer Artificial Neural Network from Scratch
  • 13. Parallelizing Neural Network Training with TensorFlow
  • 14. Going Deeper – The Mechanics of TensorFlow
  • 15. Classifying Images with Deep Convolutional Neural Networks
  • 16. Modeling Sequential Data Using Recurrent Neural Networks
  • 17. Generative Adversarial Networks for Synthesizing New Data
  • 18. Reinforcement Learning for Decision Making in Complex Environments
  • Title: Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
  • Author: Sebastian Raschka, Vahid Mirjalili
  • Original title: Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
  • ISBN: 9781789958294, 9781789958294
  • Date of issue: 2019-12-12
  • Format: Ebook
  • Item ID: e_2az9
  • Publisher: Packt Publishing