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Python Machine Learning, Second Edition - Second Edition
Python Machine Learning, Second Edition - Second Edition

Python Machine Learning, Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, Second Edition

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Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili
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R$50 per month
Book Sep 2017 622 pages 2nd Edition
eBook
R$80.00 R$196.99
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R$245.99
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Renews at R$50p/m
Arrow left icon
Profile Icon Sebastian Raschka Profile Icon Vahid Mirjalili
Arrow right icon
R$50 per month
Book Sep 2017 622 pages 2nd Edition
eBook
R$80.00 R$196.99
Print
R$245.99
Subscription
Free Trial
Renews at R$50p/m
eBook
R$80.00 R$196.99
Print
R$245.99
Subscription
Free Trial
Renews at R$50p/m

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Python Machine Learning, Second Edition - Second Edition

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Key benefits

  • 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

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Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries.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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 20, 2017
Length 622 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781787125933
Vendor :
Google
Category :

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Product Details

Publication date : Sep 20, 2017
Length 622 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781787125933
Vendor :
Google
Category :

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Table of Contents

18 Chapters
Preface Chevron down icon Chevron up icon
1. Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
2. Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
3. A Tour of Machine Learning Classifiers Using scikit-learn Chevron down icon Chevron up icon
4. Building Good Training Sets – Data Preprocessing Chevron down icon Chevron up icon
5. Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
7. Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
8. Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
9. Embedding a Machine Learning Model into a Web Application Chevron down icon Chevron up icon
10. Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
11. Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
12. Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
13. Parallelizing Neural Network Training with TensorFlow Chevron down icon Chevron up icon
14. Going Deeper – The Mechanics of TensorFlow Chevron down icon Chevron up icon
15. Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
16. Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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