Chan - Machine Learning With Python For Beginners: A Step-By-Step Guide with Hands-On Projects (Learn Coding Fast with Hands-On Project Book 7)

Machine Learning with Python for Beginners

A Step-by-Step Guide with Hands-On Projects

by Jamie Chan

https://www.learncodingfast.com/machine-learning

Copyright 2021

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other non-commercial uses permitted by copyright law.

Preface

Machine learning is becoming mainstream in recent years and has revolutionized every aspect of our lives. From facial recognition on social media platforms to route suggestions by digital maps, it is at the heart of countless technological breakthroughs.

With the increase in the use of machine learning, there has been a surge in demand for individuals with relevant skills. Perhaps you would like to add machine learning features to your projects, or you are required to pick up some machine learning skills for your job. Whatever your reason is for learning machine learning, this book aims to cover the major concepts in a step-by-step fashion and provide you with an excellent base to explore further.

The book uses a hands-on approach and includes many code examples for you to try out. In addition, youll get a chance to practice what you learned with three hands-on projects.

You can download all the code examples, images, and data files used in this book at https://www.learncodingfast.com/machine-learning .

I sincerely hope you find this book useful. If you have any questions or suggestions regarding the book, feel free to reach out to me at .

More books by Jamie

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Learn Java in One Day and Learn It Well

Web Programming Series

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Welcome to the world of machine learning! Whether you are an aspiring data scientist or just curious about machine learning, this book is designed to help you grasp the fundamental concepts of machine learning in a systematic and step-by-step fashion.

The book aims to be as beginner-friendly as possible. However, note that it has some prerequisites.

Firstly, the book assumes that you are familiar with statistical measures such as the mean, median, mode, variance, and standard deviation. If you have forgotten these concepts, you can refer to https://www.learncodingfast.com/machine-learning for a quick recap.

Next, you need to be comfortable with basic Python, especially with the concept of object-oriented programming in Python. Itll also be great if you are familiar with Python lists and dictionaries, as well be working with enhanced versions of lists and dictionaries in this book.

If you are new to Python, I strongly recommend reading my introductory book Learn Python in One Day and Learn It Well (2 nd edition) before proceeding. If you are already comfortable with Python, lets move on.

In recent years, artificial intelligence (AI) algorithms have become widely available and have fundamentally changed fields ranging from business analytics to healthcare. AI is an umbrella concept that refers to any technique that enables computers to mimic human behavior; machine learning is a subset of AI.

Machine learning is concerned with giving computers the ability to perform a task without being explicitly programmed .

As an example, suppose we want to sort emails into promotional and non-promotional emails. In conventional programming, we can do this using a set of hard-coded rules or conditional statements. For instance, one possible rule is to classify an email as promotional if it contains the words Discount, Sale, or Free Gift. We can also classify an email as non-promotional if the email address includes .gov or .edu.

The problem with such an approach is that it is challenging to come up with the rules. For instance, while most emails from addresses that contain .edu are likely to be non-promotional (such as an email from your thesis supervisor), it is also possible for educational institutions to send promotional emails advertising their courses.

It is almost impossible to come up with a set of rules that considers all possible scenarios. This is where machine learning can come in handy. Machine learning can super-charge the sorting program by identifying each emails unique attributes and autonomously derive robust rules to automate the sorting process, thereby preventing the need for manually engineered rules.

For a machine to do that, we need to provide it with data. The goal is for the machine to learn the rules directly from the data, using what are known as machine learning algorithms.

In a nutshell, machine learning algorithms are made up of formulas and procedures derived from mathematical concepts in linear algebra, calculus, probability, statistics, and other fields. These formulas and procedures are implemented in programming code and used to perform calculations on our data.

After performing the calculations, the algorithm typically generates an output known as a model . The process of generating the model is known as training the model. This model describes the rules, numbers, and any other algorithm-specific data structures that our machine learned from the data. Our machine can then use the model to perform its task.

Two of the most common tasks machine learning models perform are classification (e.g., classifying emails into promotional and non-promotional) and prediction (e.g., predicting stock prices). Other tasks include making recommendations, image recognition, and natural language processing.

As an example of how machine learning works, let us briefly discuss one of the most fundamental algorithms in machine learning: the simple linear regression algorithm. In simple linear regression, the task is to establish a linear relationship between an independent variable ( x ) and a dependent variable ( y ) and to use the relationship to make predictions. We can represent this relationship as a linear equation of the form y = a + bx .

The linear regression algorithm aims to find the best possible values for the coefficients a and b by performing calculations on the data provided. Once the calculations are done, the linear regression algorithm returns a model, including the values of a and b. Our machine can then use the equation y = a + bx (with known values of a and b) to make predictions.

Machine learning models range from simple to complex. Some complex models involve neural networks and are part of a subset of machine learning known as deep learning. These models are inspired by the structure of our brains and involve networks with multiple connected layers. We will not be covering deep learning models in this book.

There are three main approaches to machine learning: supervised learning , unsupervised learning , and reinforcement learning .

Supervised learning is typically employed when we want our machine to make predictions or classifications. In such cases, we need to provide the machine learning algorithm with labeled data.

For example, suppose we want to predict the price of a house based on its location and floor area. We can provide the machine learning algorithm with a CSV file that lists the prices of 10,000 houses, along with the location and floor area of each house.