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Book details

File Size: 16 MB

Format: epub

Print Length: 624 pages

Page Numbers Source ISBN: 1787125939

Publisher: Packt Publishing; 2 edition (September 20, 2017)

Publication Date: September 20, 2017

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B0742K7HYF



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



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



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 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



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