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Training Simple Machine Learning Algorithms for Classification

In this chapter, we will make use of two of the first algorithmically described machine learning algorithms for classification: the perceptron and adaptive linear neurons. We will start by implementing a perceptron step by step in Python and training it to classify different flower species in the Iris dataset. This will help us to understand the concept of machine learning algorithms for classification and how they can be efficiently implemented in Python.

Discussing the basics of optimization using adaptive linear neurons will then lay the groundwork for using more sophisticated classifiers via the scikit-learn machine learning library in Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn.

The topics that we will cover in this chapter are as follows:

Building an understanding of machine learning algorithms
Using pandas, NumPy, and Matplotlib to read in, process, and visualize data

Key benefits

Third edition of the bestselling, widely acclaimed Python machine learning book

Clear and intuitive explanations take you deep into the theory and practice of Python machine learning

Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices

Description

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

Who is this book for?

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

What you will learn

Master the frameworks, models, and techniques that enable machines to learn from data

Use scikit-learn for machine learning and TensorFlow for deep learning

Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more

Build and train neural networks, GANs, and other models

Discover best practices for evaluating and tuning models

Predict continuous target outcomes using regression analysis

Dig deeper into textual and social media data using sentiment analysis

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

AI Assistant (beta) to help accelerate your learning

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THEODOROS ZAFEIRIDIS Jul 16, 2024

the best among many others

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Matthew Emerick Apr 20, 2020

Disclaimer: The publisher asked me to review this book and gave me a review copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.Overview:This book is for anyone with Python experience that in interested in learning about machine learning and artificial intelligence. It gives a wide range of experience for anyone that goes through the exercises, from the fundamentals to advanced TensorFlow, GANs, and reinforcement learning. By working through this book, the reader should know enough to join a machine learning team.What I Like:This book has a wonderful breadth and depth about it, and is structured very well. The very first chapter sets realistic expectations and how to get ready for the meat of the book. The second chapter follows quite well with the basics that the rest of the book is based on.Chapters four and five are great for preparing your data, which is required for the remaining chapters. Then comes a timely chapter on tuning your hyperparameters, which is essential for gaining the best results from your algorithm. Then come several chapters that go into detail on specific applications. Chapters thirteen and fourteen detail the use of TensorFlow, which is used in the remaining chapters.The final chapters go into more advanced applications of ML using the concepts already discussed in the book. This gives a little closure to the book, but I feel like it's missing something. Maybe a chapter listing ways to extend what you've already done, or sections of other concepts that were not discussed.What I Don't Like:One of the only things that I can really point out is that while chapter three works through classification algorithms (unsupervised learning), the following two chapters go through preparing your data which comes before using any of those clustering algorithms. The only way to look at it that make sense is to see chapter three giving you even more of a follow up introduction after the previous chapter before getting into more detail. It's probably not the path I would have gone, but I'm not the author. And, really, it's a small quibble.Chapter seven is also placed a little oddly, in that it's about ensemble learning, which is where you combine techniques, which haven't been taught yet, to get better results. I would have placed this at the end, which would have been a nice way to close out the book.There is also a section in chapter fourteen about migrating from the first version of TensorFlow to the second, which is an odd thing to have in a book that is introducing the reader to the library. Why not just stick with the newer version, which will be supported longer?What I Would Like to See:There is very little I would change in this book, other than a little more consistency with using the newest version of TensorFlow and a slight reorganization. I like the summaries at the end of the chapters. Project extension ideas would have been nice, as would add a chapter with a quick summary of important concepts that the authors didn't have time to go into. But otherwise, it's an excellent book.Overall, I give this book 4.6 out of 5 stars. I applaud the work done and look forward to more from the authors.

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acc_annon Dec 18, 2019

Review of the drm-free pdf version sold by the publisher. One of the best practical books on the subject! Covering wide range of topics with concrete non-trivial practical examples, python code, data sets, and with enough-but-not-too-much theory and references to provide further insight and understanding. 750+ pages! Highly recommended.

Puneet Apr 29, 2021

Easy language book...

B Apr 12, 2021

El libro cubre un amplio contenido en Machine Learning y Deep Learning, con explicaciones muy precisas de lo que se está haciendo en cada momento, es un libro muy bueno pero recomiendo tener algunas bases, lo recomiendo si quieres profundizar en el tema o si no has entendido del todo algunos fundamentos o bases, es perfecto para estudiantes y profesionales del sector, también habrá códigos que no funcionen 100% como en el libro y tendrás que adaptarlos o matizarlos, pues los paquetes han ido actualizándose y hay pequeños cambios, en cualquier caso, es fácil resolver cualquier problema que encuentres con los códigos si consultas en internet y en foros especializados.

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

What do you get with Print?

Python Machine Learning

Training Simple Machine Learning Algorithms for Classification

Artificial neurons – a brief glimpse into the early history of machine learning

Implementing a perceptron learning algorithm in Python

An object-oriented perceptron API

Adaptive linear neurons and the convergence of learning

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

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