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Building Machine Learning Systems with Python
Building Machine Learning Systems with Python

Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow , Third Edition

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Profile Icon Pedro Coelho Profile Icon Willi Richert Profile Icon Brucher
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$31.99 $35.99
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.7 (3 Ratings)
eBook Jul 2018 406 pages 3rd Edition
eBook
$31.99 $35.99
Paperback
$43.99
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Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Pedro Coelho Profile Icon Willi Richert Profile Icon Brucher
Arrow right icon
$31.99 $35.99
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.7 (3 Ratings)
eBook Jul 2018 406 pages 3rd Edition
eBook
$31.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$31.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Building Machine Learning Systems with Python

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Key benefits

  • Use scikit-learn and TensorFlow to train your machine learning models
  • Implement popular supervised and unsupervised machine learning algorithms in Python
  • Discover best practices for building production-grade machine learning systems from scratch

Description

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI). With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance. By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

Who is this book for?

This book is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. Prior knowledge of Python programming is expected.

What you will learn

  • Build a classification system that can be applied to text, images, and sound
  • Solve regression-related problems using scikit-learn and TensorFlow
  • Recommend products to users based on their previous purchases
  • Explore different methods of applying deep neural networks to your data
  • Understand recent advances in computer vision and natural language processing (NLP)
  • Deploy Amazon Web Services (AWS) to run data models on the cloud

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2018
Length: 406 pages
Edition : 3rd
Language : English
ISBN-13 : 9781788622226
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What do you get with eBook?

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Product Details

Publication date : Jul 31, 2018
Length: 406 pages
Edition : 3rd
Language : English
ISBN-13 : 9781788622226
Category :
Languages :

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Frequently bought together


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Total $ 142.97
Python Machine Learning, Second Edition
$43.99
Machine Learning Algorithms
$54.99
Building Machine Learning Systems with Python
$43.99
Total $ 142.97 Stars icon

Table of Contents

16 Chapters
Getting Started with Python Machine Learning Chevron down icon Chevron up icon
Classifying with Real-World Examples Chevron down icon Chevron up icon
Regression Chevron down icon Chevron up icon
Classification I – Detecting Poor Answers Chevron down icon Chevron up icon
Dimensionality Reduction Chevron down icon Chevron up icon
Clustering – Finding Related Posts Chevron down icon Chevron up icon
Recommendations Chevron down icon Chevron up icon
Artificial Neural Networks and Deep Learning Chevron down icon Chevron up icon
Classification II – Sentiment Analysis Chevron down icon Chevron up icon
Topic Modeling Chevron down icon Chevron up icon
Classification III – Music Genre Classification Chevron down icon Chevron up icon
Computer Vision Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Bigger Data Chevron down icon Chevron up icon
Where to Learn More About Machine Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.7
(3 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 66.7%
1 star 33.3%
dhoelzer Oct 11, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
The first chapter or two provides a very nice introduction to the topic... but that rapidly declines. You quickly realize that the examples printed in the book are both incomplete and buggy and that the versions in the Github repository vary very significantly from the discussion in the book. By the time you deal with decision trees (really, the first exercise where you are predicting things), the author simply stops providing examples, simply saying that they are in the repo, and leaving it to the reader to figure them out and try to figure out why they work (or don't work).Additionally, early sections of the book require you to have read later sections of the book, creating a circular dependency in the book itself! Not recommended.
Amazon Verified review Amazon
Extell Farve Dec 27, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
like the title says, these books come through like they dont have an editor behind them and the code segments feel like they were never even tested, and the numerical discrepancies for some statistics despite following the book's directions to a T are frustrating
Amazon Verified review Amazon
PJ Sep 12, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I bought the kindle edition and it's been 9 days since I purchased it. I can't even return this book now and it's an absolute pain to read through. Most of the codes work but there are mistakes in some of the codes. I worked through Chapter 3 and I found at least two such cases. Sample codes are provided through github in the form of jupyter notebook and chapter 3 codes don't run as provided. Outputs for that notebook were not included, so I doubt the author even ran it once before publishing the codes. It's not hard to fix the mistakes in the code but the provided codes should work.For kindle edition, yhat is printed as ybar. That's not a big deal but it is misleading if you don't have a background in stats or other related fields.
Amazon Verified review Amazon
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