Oswald Campesato - Angular and Machine Learning Pocket Primer

ANGULAR
AND
MACHINE LEARNING

Pocket Primer

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ANGULAR
AND
MACHINE LEARNING

Pocket Primer

Oswald Campesato

Copyright 2020 by MERCURY LEARNING AND INFORMATION LLC. All rights reserved.

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O. Campesato. Angular and Machine Learning Pocket Primer.
ISBN: 9781683924708

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Id like to dedicate this book to my parents may this bring joy and happiness into their lives.

CONTENTS

PREFACE

What is the Goal?

The goal of this book is to introduce advanced beginners to basic machine learning and incorporate that knowledge into Angular 8 applications. This book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several popular machine learning classifiers. It includes code samples that are part of a university course taught by the author of this book.

This book will save you the time required to search for code samples, which is a potentially time-consuming process. If youre not sure whether you can absorb the material in this book, glance through the code samples to get a feel for the level of complexity.

At the risk of stating the obvious, please keep in mind the following point: you will not become an expert in machine learning or Angular 8 by reading this book.

What Will I Learn from This Book?

The first three chapters contain a short tour of basic Angular functionality, such as UI components and forms in Angular applications. The fourth chapter introduces you to machine learning concepts, such as supervised and unsupervised learning, followed by the major types of machine learning algorithms (regression, classification, and clustering), along with a section discussing linear regression. The fifth chapter is devoted to classification algorithms, such as kNN, Nave Bayes, decision trees, random forests, and SVM (Support Vector Machines).

The sixth chapter introduces basic TensorFlow concepts, followed by TensorFlow.js (i.e., TensorFlow in modern browsers) and some examples of Angular applications combined with machine learning. The appendix contains an introduction to Keras, along with some basic code samples.

Although Jupyter is popular, all the code samples in this book are Python scripts. However, you can quickly learn about the useful features of Jupyter through various online tutorials. In addition, its worth looking at Google Colaboratory: it is entirely online, is based on Jupyter notebooks, and offers free GPU usage.

How Much Keras Knowledge is Needed for This Book?

Some exposure to Keras is helpful, and you can read the appendix if Keras is new to you. If you also want to learn about Keras and logistic regression, there is an example in Chapter 3. This example requires some theoretical knowledge involving activation functions, optimizers, and cost functions, all of which are discussed in Chapter 4.

Please keep in mind that Keras is well-integrated into TensorFlow 2 (in the tf.keras namespace), and it provides a layer of abstraction over the pure TensorFlow that will enable you to develop prototypes more quickly.

Do I Need to Learn the Theory Portions of This Book?

Once again, the answer depends on the extent to which you plan to become involved in machine learning. In addition to creating a model, you will use various algorithms to see which ones provide the level of accuracy (or some other metric) that you need for your project. If you fall short, the theoretical aspects of machine learning can help you perform a forensic analysis of your model and your data, and ideally assist in determining how to improve your model.