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Download this book in **EPUB** and **PDF** formats

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Publication date :
Sep 20, 2017

Length
622 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781787125933

Vendor :

Google

Category :

Languages :

Concepts :

- 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

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries.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 Raschkaās 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 Mirjaliliās 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, youāll be ready to meet the new data analysis opportunities.
If youāve read the first edition of this book, youāll 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. Youāll 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.

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

Download this book in **EPUB** and **PDF** formats

Access this title in our online reader with advanced features

Publication date :
Sep 20, 2017

Length
622 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781787125933

Vendor :

Google

Category :

Languages :

Concepts :

Python Machine Learning Second Edition

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Preface

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 Sets ā 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 ā Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper ā The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

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