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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Classifying with Real-World Examples

The topic of this chapter is classification. In this setting of machine learning, you provide the system with examples of different classes of objects that you are interested in and then ask it to generalize to new examples where the class is not known. This may seem abstract, but you have probably already used this form of machine learning as a consumer, even if you were not aware of it: your email system will likely have the ability to automatically detect spam. That is, the system will analyze all incoming emails and mark them as either spam or not spam. Often, you, the end user, will be able to manually tag emails as spam or not, in order to improve its spam detection ability. This is exactly what we mean by classification: you provide examples of spam and and non-spam emails and then use an automated system to classify incoming emails...

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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

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

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dhoelzer Oct 11, 2019

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.

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Extell Farve Dec 27, 2021

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

PJ Sep 12, 2019

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.

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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Building Machine Learning Systems with Python

Classifying with Real-World Examples

The Iris dataset

Evaluation – holding out data and cross-validation

How to measure and compare classifiers

A more complex dataset and the nearest-neighbor classifier

Learning about the seeds dataset

Which classifier to use

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