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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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Kemalcan Oct 03, 2023

I find it difficult to follow. In the first chapters, there are some easy concepts that were described in a confusing way.

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AutoMan Aug 24, 2023

I have started to read the first chapter and I have found that the book code just don’t run if you don’t have the correct packages in the Python interpreter. Use the following packages versions for python 3.9.13:NumPy 1.21.2SciPy 1.7.0Scikit-learn 1.0Matplotlib 3.4.3pandas 1.3.2

Gloria Sep 10, 2022

Il manuale è scritto bene (nel caso di quello in inglese il linguaggio è decisamente semplice).Mi riservo di non consigliarlo ai neofiti della materia poiché gli argomenti non vengono spiegati “in maniera semplice”.L’ho usato per capire principalmente in quale caso è meglio usare un algoritmo invece di un altro e poi approfondire con altre fonti.Consiglio vivamente di acquistare la versione in inglese per risparmiare non poco poiché quella italiana non ne vale la pena.

Armando Carvajal Jun 27, 2022

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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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