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Python Machine Learning, Second Edition Cover

Python Machine Learning, Second Edition

Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Publisher:Packt Publishing Limited
By: Sebastian Raschka and  Vahid Mirjalili  
Paid access
|Oct 2017
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Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.

Key Features

  • Second edition of the bestselling book on Machine Learning
  • A practical approach to key frameworks in data science, machine learning, and deep learning
  • Use the most powerful Python libraries to implement machine learning and deep learning
  • Get to know the best practices to improve and optimize your machine learning systems and algorithms

Book Description

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.

If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

What you will learn

  • Understand the key frameworks in data science, machine learning, and deep learning
  • Harness the power of the latest Python open source libraries in machine learning
  • Explore machine learning techniques using challenging real-world data
  • Master deep neural network implementation using the TensorFlow 1.x library
  • Learn the mechanics of classification algorithms to implement the best tool for the job
  • Predict continuous target outcomes using regression analysis
  • Uncover hidden patterns and structures in data with clustering
  • Delve deeper into textual and social media data using sentiment analysis

Who this book is for

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Machine Learning Algorithms the Ability to Learn from Data
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn
  4. Building Good Training Sets – 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
  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
https://github.com/packtpublishing/python-machine-learning-second-edition
PDF ISBN: 978-1-78712-602-2
Publisher: Packt Publishing Limited
Copyright owner: © 2017 Packt Publishing Limited
Publication date: 2017
Language: English
Pages: 622
Related subjects:
Computer sciences,
Artificial intelligence

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