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by: Sebastian Raschka, Vahid Mirjalili

 : Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

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Binding: Paperback
EAN: 9781787125933
Edition: 2nd
ISBN: 1787125939
Item Dimensions: 925750207141
Label: Packt Publishing - ebooks Account
Languages: EnglishPublishedEnglishOriginal LanguageEnglishUnknown
Manufacturer: Packt Publishing - ebooks Account
Number Of Items: 1
Number Of Pages: 622
Publication Date: September 20, 2017
Publisher: Packt Publishing - ebooks Account
Release Date: September 20, 2017
Studio: Packt Publishing - ebooks Account




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Key Features

Book Description



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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world.



If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

What you will learn

Table of Contents

  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 - 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




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