The Supervised Learning Workshop

A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

• Editorial: Packt Publishing
• Año: 2020
• ISBN:
  9781800208322
• Edición: 2
• Categoría: Computer Science
• Categoría: Programming in Python

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms

Key Features

• Ideal for those getting started with machine learning for the first time
• A step-by-step machine learning tutorial with exercises and activities that help build key skills
• Structured to let you progress at your own pace, on your own terms
• Use your physical print copy to redeem free access to the online interactive edition

Book Description

You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results.

Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.

Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book.

Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

What you will learn

• Get to grips with the fundamental of supervised learning algorithms
• Discover how to use Python libraries for supervised learning
• Learn how to load a dataset in pandas for testing
• Use different types of plots to visually represent the data
• Distinguish between regression and classification problems
• Learn how to perform classification using K-NN and decision trees

Who this book is for

Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.

1. Fundamentals

Overview

This chapter introduces you to supervised learning, using Anaconda to manage coding environments, and using Jupyter notebooks to create, manage, and run code. It also covers some of the most common Python packages used in supervised learning: pandas, NumPy, Matplotlib, and seaborn. By the end of this chapter, you will be able to install and load Python libraries into your development environment for use in analysis and machine learning problems. You will also be able to load an external data source using pandas, and use a variety of methods to search, filter, and compute descriptive statistics of the data. This chapter will enable you to gauge the potential impact of various issues such as missing data, class imbalance, and low sample size within the data source.

Introduction

The study and application of machine learning and artificial intelligence has recently been the source of much interest and research in the technology and business communities. Advanced data analytics and machine learning techniques have shown great promise in advancing many sectors, such as personalized healthcare and self-driving cars, as well as in solving some of the world's greatest challenges, such as combating climate change (see Tackling Climate Change with Machine Learning: https://arxiv.org/pdf/1906.05433.pdf).

This book has been designed to help you to take advantage of the unique confluence of events in the field of data science and machine learning today. Across the globe, private enterprises and governments are realizing the value and efficiency of data-driven products and services. At the same time, reduced hardware costs and open source software solutions are significantly reducing the barriers to entry of learning and applying machine learning techniques.

Here, we will focus on supervised machine learning (or, supervised learning for short). We'll explain the different types of machine learning shortly, but let's begin with some quick information. The now-classic example of supervised learning is developing an algorithm to distinguish between pictures of cats and dogs. The supervised part arises from two aspects; first, we have a set of pictures where we know the correct answers. We call such data labeled data. Second, we carry out a process where we iteratively test our algorithm's ability to predict "cat" or "dog" given pictures, and we make corrections to the algorithm when the predictions are incorrect. This process, at a high level, is similar to teaching children. However, it generally takes a lot more data to train an algorithm than to teach a child to recognize cats and dogs! Fortunately, there are rapidly growing sources of data at our disposal. Note the use of the words learning and train in the context of developing our algorithm. These might seem to be giving human qualities to our machines and computer programs, but they are already deeply ingrained in the machine learning (and artificial intelligence) literature, so let's use them and understand them. Training in our context here always refers to the process of providing labeled data to an algorithm and making adjustments to the algorithm to best predict the labels given the data. Supervised means that the labels for the data are provided within the training, allowing the model to learn from these labels.

Let's now understand the distinction between supervised learning and other forms of machine learning.

When to Use Supervised Learning

Generally, if you are trying to automate or replicate an existing process, the problem is a supervised learning problem. As an example, let's say you are the publisher of a magazine that reviews and ranks hairstyles from various time periods. Your readers frequently send you far more images of their favorite hairstyles for review than you can manually process. To save some time, you would like to automate the sorting of the hairstyle images you receive based on time periods, starting with hairstyles from the 1960s and 1980s, as you can see in the following figure:

Figure 1.1: Images of hairstyles from different time periods

To create your hairstyles-sorting algorithm, you start by collecting a large sample of hairstyle images and manually labeling each one with its corresponding time period. Such a dataset (known as a labeled dataset) is the input data (hairstyle images) for which the desired output information (time period) is known and recorded. This type of problem is a classic supervised learning problem; we are trying to develop an algorithm that takes a set of inputs and learns to return the answers that we have told it are correct.

Python Packages and Modules

Python is one of the most popular programming languages used for machine learning, and is the language used here.

While the standard features that are included in Python are certainly feature-rich, the true power of Python lies in the additional libraries (also known as packages), which, thanks to open source licensing, can be easily downloaded and installed through a few simple commands. In this book, we generally assume your system has been configured using Anaconda, which is an open source environment manager for Python. Depending on your system, you can configure multiple virtual environments using Anaconda, each one configured with specific packages and even different versions of Python. Using Anaconda takes care of many of the requirements to get ready to perform machine learning, as many of the most common packages come pre-built within Anaconda. Refer to the preface for Anaconda installation instructions.

In this book, we will be using the following additional Python packages:

• NumPy (pronounced Num Pie and available at https://www.numpy.org/): NumPy (short for numerical Python) is one of the core components of scientific computing in Python. NumPy provides the foundational data types from which a number of other data structures derive, including linear algebra, vectors and matrices, and key random number functionality.
• SciPy (pronounced Sigh Pie and available at https://www.scipy.org): SciPy, along with NumPy, is a core scientific computing package. SciPy provides a number of statistical tools, signal processing tools, and other functionality, such as Fourier transforms.
• pandas (available at https://pandas.pydata.org/): pandas is a high-performance library for loading, cleaning, analyzing, and manipulating data structures.
• Matplotlib (available at https://matplotlib.org/): Matplotlib is the foundational Python library for creating graphs and plots of datasets and is also the base package from which other Python plotting libraries derive. The Matplotlib API has been designed in alignment with the Matlab plotting library to facilitate an easy transition to Python.
• Seaborn (available at https://seaborn.pydata.org/): Seaborn is a plotting library built on top of Matplotlib, providing attractive color and line styles as well as a number of common plotting templates.
• Scikit-learn (available at https://scikit-learn.org/stable/)...

Índice

• The Supervised Learning Workshop
• Preface
• 1. Fundamentals
• 2. Exploratory Data Analysis and Visualization
• 3. Linear Regression
• 4. Autoregression
• 5. Classification Techniques
• 6. Ensemble Modeling
• 7. Model Evaluation
• Appendix