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Python Machine Learning By Example

Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems , Second Edition

Yuxi (Hayden) Liu

€15.99 ~~€23.99~~

eBook Feb 2019 382 pages 2nd Edition

€15.99 ~~€23.99~~

Renews at €18.99p/m

Yuxi (Hayden) Liu

€15.99 ~~€23.99~~

eBook Feb 2019 382 pages 2nd Edition

€15.99 ~~€23.99~~

Renews at €18.99p/m

€15.99 ~~€23.99~~

Renews at €18.99p/m

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Key benefits

Exploit the power of Python to explore the world of data mining and data analytics
Discover machine learning algorithms to solve complex challenges faced by data scientists today
Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects

Description

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

Who is this book for?

If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

What you will learn

Understand the important concepts in machine learning and data science
Use Python to explore the world of data mining and analytics
Scale up model training using varied data complexities with Apache Spark
Delve deep into text and NLP using Python libraries such NLTK and gensim
Select and build an ML model and evaluate and optimize its performance
Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

OR

Product Details

Publication date : Feb 28, 2019

Length: 382 pages

Edition : 2nd

Language : English

ISBN-13 : 9781789617559

Vendor :

Google

Category :

Data

Languages :

Concepts :

Machine Learning

Tools :

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Table of Contents

14 Chapters

Section 1: Fundamentals of Machine Learning

Section 1: Fundamentals of Machine Learning

Getting Started with Machine Learning and Python

Getting Started with Machine Learning and Python

Defining machine learning and why we need it

A very high-level overview of machine learning technology

Core of machine learning – generalizing with data

Preprocessing, exploration, and feature engineering

Combining models

Installing software and setting up

Section 2: Practical Python Machine Learning By Example

Section 2: Practical Python Machine Learning By Example

Exploring the 20 Newsgroups Dataset with Text Analysis Techniques

Exploring the 20 Newsgroups Dataset with Text Analysis Techniques

How computers understand language - NLP

Picking up NLP basics while touring popular NLP libraries

Getting the newsgroups data

Exploring the newsgroups data

Thinking about features for text data

Visualizing the newsgroups data with t-SNE

Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms

Learning without guidance – unsupervised learning

Clustering newsgroups data using k-means

Discovering underlying topics in newsgroups

Topic modeling using NMF

Topic modeling using LDA

Detecting Spam Email with Naive Bayes

Detecting Spam Email with Naive Bayes

Getting started with classification

Exploring Naïve Bayes

Classification performance evaluation

Model tuning and cross-validation

Classifying Newsgroup Topics with Support Vector Machines

Classifying Newsgroup Topics with Support Vector Machines

Finding separating boundary with support vector machines

Classifying newsgroup topics with SVMs

More example – fetal state classification on cardiotocography

A further example – breast cancer classification using SVM with TensorFlow

Predicting Online Ad Click-Through with Tree-Based Algorithms

Predicting Online Ad Click-Through with Tree-Based Algorithms

Brief overview of advertising click-through prediction

Getting started with two types of data – numerical and categorical

Exploring decision tree from root to leaves

Implementing a decision tree from scratch

Predicting ad click-through with decision tree

Ensembling decision trees – random forest

Predicting Online Ad Click-Through with Logistic Regression

Predicting Online Ad Click-Through with Logistic Regression

Converting categorical features to numerical – one-hot encoding and ordinal encoding

Classifying data with logistic regression

Training a logistic regression model

Training on large datasets with online learning

Handling multiclass classification

Implementing logistic regression using TensorFlow

Feature selection using random forest

Scaling Up Prediction to Terabyte Click Logs

Scaling Up Prediction to Terabyte Click Logs

Learning the essentials of Apache Spark

Programming in PySpark

Learning on massive click logs with Spark

Feature engineering on categorical variables with Spark

Stock Price Prediction with Regression Algorithms

Stock Price Prediction with Regression Algorithms

Brief overview of the stock market and stock prices

What is regression?

Mining stock price data

Estimating with linear regression

Estimating with decision tree regression

Estimating with support vector regression

Estimating with neural networks

Evaluating regression performance

Predicting stock price with four regression algorithms

Section 3: Python Machine Learning Best Practices

Section 3: Python Machine Learning Best Practices

Machine Learning Best Practices

Machine Learning Best Practices

Machine learning solution workflow

Best practices in the data preparation stage

Best practices in the training sets generation stage

Best practices in the model training, evaluation, and selection stage

Best practices in the deployment and monitoring stage

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Customer reviews

Rating distribution

5

(2 Ratings)

5 star 100%

4 star 0%

3 star 0%

2 star 0%

1 star 0%

crystalattice Nov 22, 2020

5

Python ML By Example (BE) is a good complement to Python ML Third Edition (3E). The 3E book focuses on the theory and general application of ML programming, while the BE book focuses an specific application examples.While they both tackle ML programming, their approach is different. The BE book assumes you have a reasonable, foundational background in ML and uses that basis to create specific ML-based applications.For example, whereas 3E has a simple note about Naïve Bayes classification, the BE book has a whole chapter dedicated to the algorithm, discussing the different types of classification methods, how Naïve Bayes works, and then actually implementing a Naïve Bayes application. On the flip side, the 3E book has a whole chapter dedicated just to the different classifiers and different implementations of them using scikit-learn.It's almost like the 3E book is a textbook and the BE book is its complementary workbook for practice. While you may be able to be successful with either one, combining them really maximizes your ML learning.To speak about the BE book in more detail, the topics covered include:*Introduction to Python ML, including software installation*Using Naïve Bayes algorithm to create movie recommendation application*Using SVM for facial recognition*Using tree-based algorithms to predict ad click-through*Using Apache Spark to work with large data sets*Using regression algorithms and neural networks to predict the stock market*Using text analysis and NLP to data mine newsgroups*Using unsupervised learning models to identify newsgroups topics*Using different types of neural networks for different types of analysis approaches*Using reinforcement learning for decision making*ML best practicesIt is a long book (nearly 500 pages), but the material is invaluable for anyone in the ML field, especially if you don't have a lot of experience with the different algorithms. And in conjunction with 3E, you almost have a complete ML curriculum.

Amazon Verified review

Sunil Thapa Mar 03, 2020

5

Great book to review some machine learning algorithms l.

Amazon Verified review

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About the author

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.

See other products by Yuxi (Hayden) Liu

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