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    Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python
    Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python
    Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python
    Ebook176 pages2 hours

    Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python

    By Brady Ellison

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    About this ebook

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    Ready to discover the Machine Learning world?
    Machine learning paves the path into the future and it's powered by Python. All industries can benefit from machine learning and artificial intelligence whether we're talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do?
    Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data.
    Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks.
    By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don't need to be intimidated by mathematics.
    The Topics Covered Include:

    Machine learning fundamentals

    How to set up the development environment

    How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy

    How to explore data

    How to solve regression and classification problems

    Decision trees

    k-means clustering

    Feed-forward and recurrent neural networks



    Get your copy now

    LanguageEnglish
    PublisherWhiteFlowerPublsihing
    Release dateAug 25, 2021
    ISBN9798201286149
    Python Machine Learning: A Step by Step Beginner’s Guide to Learn Machine Learning Using Python

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      Book preview

      Python Machine Learning - Brady Ellison

      Introduction

      ​Nowadays, machine learning has a few different meanings, especially due to misunderstandings caused by the entertainment industry. People use too much of their imagination thinking that machine learning involves machines that take over the world due to their ability to think beyond the capability of a human using powerful algorithms. Naturally, the real world is disappointing when compared to such a vivid imagination, but that doesn’t change the fact that machine learning is still an incredible tool that takes our technology to the next level.

      ​That being said, machine learning may not have anything to do with intelligent robots, but you can perform some practical feats using various techniques and algorithms. Python Machine Learning provides you with a fundamental view of this field, handing you all the tools you need to perform amazing things. Despite some of the exercises and concepts feeling boring and mundane, once you learn them, you’ll realize that they will enable you to build useful applications that can help people and society as a whole. So, enter the ever-expanding world of machine learning with the help of this step-by-step guide!

      About this Book

      The main goal of this book is to teach you how machine learning can be used in the real world and what it can do for you if you invest enough time and practice into it. Take note that you don’t need a degree in computer science, mathematics, or be a data scientist to understand the concepts and techniques used in this book. In fact, all you really need is a bit of Python programming knowledge and some mathematics. Anyone can learn this. You’re going to learn a lot of machine learning theory, but you will also go through a number of practical demonstrations that will show you how to work with data and how to obtain results from it.

      In this book, a good deal of the focus goes on working with the right tools. You’re going to use Python as the programming language of choice, however, the theory you learn can also be applied using R if at some point in the future you want to try something new. However, the emphasis is on Python when learning machine learning concepts because Python comes with a rich selection of free libraries, packages, and other tools that make the implementation of machine algorithms much easier.

      In addition, you will explore some of the most important concepts and techniques in machine learning. You’ll learn a series and algorithms, see how they work, and find ways to improve them for better results. The main advantage of using this book is that all of these concepts can be learned with a limited knowledge of math, but it’s still recommended for you to focus on the math side as well, especially if you want to progress.

      Machine learning is complex, there are a lot of tools involved, but Python Machine Learning will guide you every step of the way and get you started as soon as possible by working with real-world datasets and tools.

      Note on Code and Datasets

      In this text, you’re going to use Python as the main language and work with a series of datasets and machine learning algorithms. We won’t dig into mathematics too much because it’s expected of you to have some basic knowledge. However, just in case you don’t, the theory will be explained and broken down into easy to understand components. That’s no reason to ignore that side. Math is a tool as much as Python, so don’t neglect it for too long or it will come back to haunt you at some point.

      That being said, all the tools, libraries, packages, and datasets we’re going to use for the purpose of this book are open source and free to use. They also come with their own documentation and it’s recommended for you to read through it to gain a deeper understanding of what you’re working with. The reason we’re using open source and community driven software is because they’re easy to access, well-documented, and very popular in the field of machine learning and data science.

      The same thing goes for the dataset. To apply machine learning algorithms and techniques, you need data. A lot of data is locked away or sold at a hefty price, so you need to scrape it yourself or find it in the form of free organized datasets. Fortunately, the Python libraries we’re going to use, like Scikit-learn, contain a number of datasets such Boston Housing and the Iris datasets.

      Before getting started with supervised machine learning algorithms and other concepts, you’re going to learn more about this field and how to install all the tools you need. It’s recommended for you to follow along and install the same libraries and packages. Alternatives exist and you can choose them if you prefer, but that’s recommended only if you’re willing to read additional documentation and adapt the examples presented in this book.

      Requirements

      Knowledge in math is essential in machine learning. Sooner or later, you need to focus your energy on it. If you aspire to become a machine learner, even at the hobbyist level, you will have to learn some mathematics. Even though some of the Python packages we’re going to use provide you with the tools needed to create a model and have everything you need for data visualization, math is needed to improve the models and achieve good results. You can easily build a model, but it takes having a mathematical foundation to interpret the results and fully understand what’s happening.

      To get you started, you should focus on the following math skills:

      Probability and statistics: Concepts that are part of probability and statistics are needed for feature visualization, data processing, dimensionality reduction, model evaluation, and much more. You will have to familiarize yourself with the median, mean, mode, standard deviation, covariance matrix, Bayes theorem, and the mean square error at least.

      Multivariable calculus: Nearly every machine learning model is created using a dataset that contains features. Therefore, you need to understand the following multivariable calculus topics: gradients, the Sigmoid function, rectifier linear unit function, minimum and maximum values, function plotting, and cost function.

      Linear algebra: This is one of the most prevalent mathematical topic that feature in this discipline. You’ll need to familiarize yourself with vectors, matrices, eigenvalues, and eigenvectors.

      Optimization methods: Because machine learning algorithms use predictive modeling to discover the weights have to be applied to the test data, you need to understand the following concepts: cost function, error function, and objection function. It also helps if you’re already familiar with the gradient descent and stochastic gradient descent algorithms

      To summarize, you need to understand the essentials in mathematics to understand machine learning theory and know how to improve your models. If you lack these foundations, you can take various courses on the side and in the meantime try to learn and apply the machine learning models and data processing techniques.

      Chapter 1: Understanding Machine Learning

      When people first hear about machine learning, they think about artificial intelligence that is designed to understand and replicate human behavior and way of thinking. While this is a crude concept bordering sci-fi, it’s not that far from the truth. For the past few decades, many algorithms were developed to train machines in using data so that they can improve the way we use technology and process information.

      Now, the question is, how are computers able to learn? How does the machine learning process work? We are going to explore these questions in this chapter to understand what this field is really about. You’ll discover that machine learning is a powerful tool that can be used just like any other.

      What is Machine Learning?

      In simple terms, machine learning is a collection of algorithms that data scientists and analysts use to create software capable of predicting an outcome based on existing information. This means that we have applications that can be trained using existing data to make some form of prediction and lead to a reliable result. Machine learning algorithms look at available data, they learn from it, make a series of adjustments to limit outliers and erroneous data, and produce an accurate outcome. Therefore, we no longer analyze enormous amounts of recorded information on our own because these algorithms can often do it with very little human involvement or even none. The beauty of machine learning is that we can use old data to teach software how to predict an accurate result when working with present data. In a way, with these algorithms, we can train programs to gain experience like we do, by learning from past experiences to make better choices in the future.

      All that being said, machine learning isn’t a new idea. It may sound futuristic to some, but the concept of machines processing information and producing results first appeared a few centuries ago. It all started during the seventeenth century when Blaise Pascal, a mathematician, designed a mechanical calculator for simple mathematical operations. During the same period, Gottfried Leibniz, another famous mathematician, designed the binary system on which computers are based even today.

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