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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Introduction to Machine Learning

Machine learning is everywhere. When you book a flight ticket, an algorithm decides the price you are going to pay for it. When you apply for a loan, machine learning may decide whether you are going to get it or not. When you scroll through your Facebook timeline, it picks which advertisements to show to you. Machine learning also plays a big role in your Google search results. It organizes your email's inbox and filters out spam, it goes through your resumé before recruiters when you apply for a job, and, more recently, it has also started to play the role of your personal assistant in the form of Siri and other virtual assistants.

In this book, we will learn about the theory and practice of machine learning. We will understand when and how to apply it. To get started, we will look at a high-level introduction to how machine learning works. You will then be able to differentiate...

Understanding machine learning

You may be wondering how machines actually learn. To get the answer to this query, let's take the following example of a fictional company. Space Shuttle Corporation has a few space vehicles to rent. They get applications every day from clients who want to travel to Mars. They are not sure whether those clients will ever return the vehicles—maybe they'll decide to continue living on Mars and never come back again. Even worse, some of the clients may be lousy pilots and crash their vehicles on the way. So, the company decides to hire shuttle rent-approval officers whose job is to go through the applications and decide who is worthy of a shuttle ride. Their business, however, grows so big that they need to formulate the shuttle-approval process.

A traditional shuttle company would start by having business rules and hiring junior employees to execute those rules. For example, if you are an alien, then sorry, you cannot rent...

The model development life cycle

When asked to solve a problem using machine learning, data scientists achieve this by following a sequence of steps. In this section, we are going to discuss those iterative steps.

Understanding a problem

"All models are wrong, but some are useful."
– George Box

The first thing to do when developing a model is to understand the problem you are trying to solve thoroughly. This not only involves understanding what problem you are solving, but also why you are solving it, what impact are you expecting to have, and what the currently available solution isthat you are comparing your new solution to. My understanding of what Box said when he stated that all models are wrong is that a model is just an approximation of reality by modeling one or more angles of it. By understanding the problem you are trying to solve, you can decide which angles of reality you need to model, and which ones you can tolerate...

Introduction to scikit-learn

Since you have already picked up this book, you probably don't need me to convince you why machine learning is important. However, you may still have doubts about why to use scikit-learn in particular. You may encounter names such as TensorFlow, PyTorch, and Spark more often during your daily news consumption than scikit-learn. So, let me convince you of my preference for the latter.

It plays well with the Python data ecosystem

scikit-learn is a Python toolkit built on top of NumPy, SciPy, and Matplotlib. These choices mean that it fits well into your daily data pipeline. As a data scientist, Python is most likely your language of choice since it is good for both offline analysis and real-time implementations. You will also be using tools such as pandas to load data from your database, which allows you to perform a vast amount of transformation to your data. Since both pandas and scikit-learn are built on top of NumPy, they play...

Installing the packages you need

It's time to install the packages we will need in this book, but first of all, make sure you have Python installed on your computer. In this book, we will be using Python version 3.6. If your computer comes with Python 2.x installed, then you should upgrade Python to version 3.6 or later. I will show you how to install the required packages using pip, Python's de facto package-management system. If you use other package-management systems, such as Anaconda, you can easily find the equivalent installation commands for each of the following packages online.

To install scikit-learn, run the following command:

          $ pip install --upgrade scikit-learn==0.22
        

I will be using version 0.22 of scikit-learn here. You can add the --userswitch to the pip command to limit the installation to your own directories. This is important if you do not have root access to your machine or if you do not want to install...

Summary

Mastering machine learning is a desirable skill nowadays given its vast application everywhere, from business to academia. Nevertheless, just understanding the theory of it will only take you so far since practitioners also need to understand their tools to be self-sufficient and capable.

In this chapter, we started with a high-level introduction to machine learning and learned when to use each of the machine learning types; from classification and regression to clustering and reinforcement learning. We then learned about scikit-learn and why practitioners recommend it when solving both supervised and unsupervised learning problems. To keep this book self-sufficient, we also covered the basics of data manipulation for those who haven't used libraries such as pandas and Matplotlib before. In the following chapters, we will continue to combine our understanding of the underlying theory of machine learning with more practical examples using scikit-learn.

...

Further reading

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Key benefits

  • Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
  • Master the art of data-driven problem-solving with hands-on examples
  • Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms

Description

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.

Who is this book for?

This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

What you will learn

  • Understand when to use supervised, unsupervised, or reinforcement learning algorithms
  • Find out how to collect and prepare your data for machine learning tasks
  • Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
  • Apply supervised and unsupervised algorithms to overcome various machine learning challenges
  • Employ best practices for tuning your algorithm's hyper parameters
  • Discover how to use neural networks for classification and regression
  • Build, evaluate, and deploy your machine learning solutions to production

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Publication date : Jul 24, 2020
Length: 384 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826048
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Product Details

Publication date : Jul 24, 2020
Length: 384 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826048
Category :
Languages :

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Table of Contents

17 Chapters
Section 1: Supervised Learning Chevron down icon Chevron up icon
Introduction to Machine Learning Chevron down icon Chevron up icon
Making Decisions with Trees Chevron down icon Chevron up icon
Making Decisions with Linear Equations Chevron down icon Chevron up icon
Preparing Your Data Chevron down icon Chevron up icon
Image Processing with Nearest Neighbors Chevron down icon Chevron up icon
Classifying Text Using Naive Bayes Chevron down icon Chevron up icon
Section 2: Advanced Supervised Learning Chevron down icon Chevron up icon
Neural Networks – Here Comes Deep Learning Chevron down icon Chevron up icon
Ensembles – When One Model Is Not Enough Chevron down icon Chevron up icon
The Y is as Important as the X Chevron down icon Chevron up icon
Imbalanced Learning – Not Even 1% Win the Lottery Chevron down icon Chevron up icon
Section 3: Unsupervised Learning and More Chevron down icon Chevron up icon
Clustering – Making Sense of Unlabeled Data Chevron down icon Chevron up icon
Anomaly Detection – Finding Outliers in Data Chevron down icon Chevron up icon
Recommender System – Getting to Know Their Taste Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(4 Ratings)
5 star 75%
4 star 25%
3 star 0%
2 star 0%
1 star 0%
Adam Powell Sep 21, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The perfect read for an analyst that wants to transition into machine learning. It broadly covers all the key algorithms with an insightful practitioner's perspective. Highly recommended!
Amazon Verified review Amazon
Sara Oct 25, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an excellent introductory book for data scientists learning machine learning and any curious people wanting to know what it takes to develop and build machine learning algorithms.The book covers the basic concepts, the terminology, and goes into detail about implementing these algorithms using Python and one of the most well-known machine learning libraries Scikit-learn.The book introduces these complex concepts is straightforward and easy to understand, even if the reader doesn't have any maths background.It contains many real-life applications of machine learning with code that the reader can follow and use to build more significant applications.I would recommend this book to anyone getting started with machine learning. This book will give you the concrete knowledge you need to start building your own machine learning applications.
Amazon Verified review Amazon
Przemyslaw Chojecki Sep 02, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you've already did a couple of data science projects, had a basic understanding of Python, did some visualisation and want to go deeper into some details of what it means to analyse data, then this book is for you.This is a practical guide to both supervised and unsupervised learning with plenty of examples in code.The main focus is on imperfect data and how to make sense of these imperfections through various machine learning algorithms.The author discusses standard data science algorithms using scikit-learn library which gives a coherent overview of the subjest. You will learn decision trees, KNN classification, Naive Bayes and much more; applied to classical datasets like Iris dataset, Boston housing prices or Fashion-MNIST.Recommended for beginning data scientists!
Amazon Verified review Amazon
Matthew Emerick Aug 10, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
About This BookThis is a guide for newcomers to machine learning who want to start learning one of the core Python ML libraries. This book focuses on scikit-learn, though discusses other related libraries as needed. We see several algorithms developed from the ground up, which aids in understanding what is going on and why things work the way they do.Who is This For?The author makes the following claim, taken directly from the preface: "This book is for machine learning data scientists who want to master the theoretical and practical sides of machine learning algorithms and understand how to use them to solve real-life problems." This a big claim for a 384 page introductory book.Why Was This Written?scikit-learn is a powerful library that is essential for machine learning in Python. Every ML developer needs to learn the basics, and this can be a good book for that. Several machine learning algorithms, both supervised and unsupervised, are covered in a fair amount of detail. As was mentioned, the book intends the reader to master machine learning and does indeed introduce the reader to important terms, algorithms, and some of the theory.OrganizationThe book starts out with a very brief introduction to machine learning, finishing up the chapter with instructions on how to set up your computer to run the included code. The book then surges forward by discussing its first algorithm. I did find it odd that a chapter on preparing your data is found after the first couple algorithms. The book focuses on supervised machine learning with only the last three chapters discussing unsupervised learning.Did This Book Succeed?I think that the computer setup would have been better left in the preface while the ML introduction could have been expanded. After reading this book, the reader will have an understanding of a selection of algorithms, enough to get a junior software developer role on a team, but will need to dig deeper to a better understanding. If you simply want to learn a bit about the subject, then this is a good book.Rating and Final ThoughtsThis is a useful book, but I feel it misses the mark by trying to do too many things. If one were to truly master machine learning, this book might be an aid early on, but is only one step. Important concepts were discussed, and even bolded, but there was no glossary at the end to make it easy to look up the terms later on; you have to use the index to find where the term was originally used and look there. There are a great many algorithms not discussed and theory left out.I have to give this book a 3.5 out of 5. It's useful, especially to the ML beginner, but it misses its own goal. A cookbook style might have worked better, focusing on either ML or scikit-learn.
Amazon Verified review Amazon
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