The Machine Learning Workshop

Get ready to develop your own high-performance machine learning algorithms with scikit-learn, 2nd Edition

Hyatt Saleh

• Publisher: Packt Publishing
• Year: 2020
• ISBN:
  9781838985462
• Edition: 2
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

Take a comprehensive and step-by-step approach to understanding machine learning

Key Features

• Discover how to apply the scikit-learn uniform API in all types of machine learning models
• Understand the difference between supervised and unsupervised learning models
• Reinforce your understanding of machine learning concepts by working on real-world examples

Book Description

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.

The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.

By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

What you will learn

• Understand how to select an algorithm that best fits your dataset and desired outcome
• Explore popular real-world algorithms such as K-means, Mean-Shift, and DBSCAN
• Discover different approaches to solve machine learning classification problems
• Develop neural network structures using the scikit-learn package
• Use the NN algorithm to create models for predicting future outcomes
• Perform error analysis to improve your model's performance

Who this book is for

The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.

1. Introduction to Scikit-Learn

Overview

This chapter introduces the two main topics of this book: machine learning and scikit-learn. By reading this book, you will learn about the concept and application of machine learning. You will also learn about the importance of data in machine learning, as well as the key aspects of data preprocessing to solve a variety of data problems. This chapter will also cover the basic syntax of scikit-learn. By the end of this chapter, you will have a firm understanding of scikit-learn's syntax so that you can solve simple data problems, which will be the starting point for developing machine learning solutions.

Introduction

Machine learning (ML), without a doubt, is one of the most relevant technologies nowadays as it aims to convert information (data) into knowledge that can be used to make informed decisions. In this chapter, you will learn about the different applications of ML in today's world, as well as the role that data plays. This will be the starting point for introducing different data problems throughout this book that you will be able to solve using scikit-learn.

Scikit-learn is a well-documented and easy-to-use library that facilitates the application of ML algorithms by using simple methods, which ultimately enables beginners to model data without the need for deep knowledge of the math behind the algorithms. Additionally, thanks to the ease of use of this library, it allows the user to implement different approximations (that is, create different models) for a data problem. Moreover, by removing the task of coding the algorithm, scikit-learn allows teams to focus their attention on analyzing the results of the model to arrive at crucial conclusions.

Spotify, a world-leading company in the field of music streaming, uses scikit-learn because it allows them to implement multiple models for a data problem, which are then easily connected to their existing development. This process improves the process of arriving at a useful model, while allowing the company to plug them into their current app with little effort.

On the other hand, booking.com uses scikit-learn due to the wide variety of algorithms that the library offers, which allows them to fulfill the different data analysis tasks that the company relies on, such as building recommendation engines, detecting fraudulent activities, and managing the customer service team.

Considering the preceding points, this chapter also explains scikit-learn and its main uses and advantages, and then moves on to provide a brief explanation of the scikit-learn Application Programming Interface (API) syntax and features. Additionally, the process of representing, visualizing, and normalizing data will be shown. The aforementioned information will help us to understand the different steps that need to be taken to develop a ML model.

In the following chapters in this book, you will explore the main ML algorithms that can be used to solve real-life data problems. You will also learn about different techniques that you can use to measure the performance of your algorithms and how to improve them accordingly. Finally, you will explore how to make use of a trained model by saving it, loading it, and creating APIs.

Introduction to Machine Learning

Machine learning (ML) is a subset of Artificial Intelligence (AI) that consists of a wide variety of algorithms capable of learning from the data that is being fed to them, without being specifically programmed for a task. This ability to learn from data allows the algorithms to create models that are capable of solving complex data problems by finding patterns in historical data and improving them as new data is fed to the models.

These different ML algorithms use different approximations to solve a task (such as probability functions), but the key element is that they are able to consider a countless number of variables for a particular data problem, making the final model better at solving the task than humans are. The models that are created using ML algorithms are created to find patterns in the input data so that those patterns can be used to make informed predictions in the future.

Applications of ML

Some of the popular tasks that can be solved using ML algorithms are price/demand predictions, product/service recommendation, and data filtering, among others. The following is a list of real-life examples of such tasks:

• On-demand price prediction: Companies whose services vary in price according to demand can use ML algorithms to predict future demand and determine whether they will have the capability to meet it. For instance, in the transportation industry, if future demand is low (low season), the price for flights will drop. On the other hand, is demand is high (high season), flights are likely to increase in price.
• Recommendations in entertainment: Using the music that you currently use, as well as that of the people similar to you, ML algorithms can construct models capable of suggesting new records that you may like. That is also the case of video streaming applications, as well as online bookstores.
• Email filtering: ML has been used for a while now in the process of filtering incoming emails in order to separate spam from your desired emails. Lately, it also has the capability to sort unwanted emails into more categories, such as social and promotions.

Choosing the Right ML Algorithm

When it comes to developing ML solutions, it is important to highlight that, more often than not, there is no one solution for a data problem, much like there is no algorithm that fits all data problems. According to this and considering that there is a large quantity of algorithms in the field of ML, choosing the right one for a certain data problem is often the turning point that separates outstanding models from mediocre ones.

The following steps can help narrow down the algorithms to just a few:

1. Understand your data: Considering that data is the key to being able to develop any ML solutions, the first step should always be to understand it in order to be able to filter out any algorithm that is unable to process such data.
   For instance, considering the quantity of features and observations in your dataset, it is possible to determine whether an algorithm capable of producing outstanding results with a small dataset is required. The number of instances/features to consider a dataset small depends on the data problem, the quantity of the outputs, and so on. Moreover, by understanding the types of fields in your dataset, you will also be able to determine whether you need an algorithm capable of working with categorical data.
2. Categorize the data problem: As per the following diagram, in this step, you should analyze your input data to determine if it contains a target feature (a feature whose values you want to be modeled and predicted) or not. Datasets with a target feature are also known as labeled data and are solved using supervised learning (A) algorithms. On the other hand, datasets without a target feature are known as unlabeled data and are solved using unsupervised learning algorithms (B).
   Moreover, the output data (the form of output that you expect from the model) also plays a key role in determining the algorithms to be used. If the output from the model needs to be a continuous number, the task to be solved is a regression problem (C). On the other hand, if the output is a discrete value (a set of categories, for instance), the task at hand is a classification problem (D). Finally, if the output is a subgroup of observations, the process to be performed is a clustering task (E):
   
   Figure 1.1: Demonstrating the division of tasks
   This division of tasks will be explored in more detail in the Supervised and Unsupervised Learning section of this chapter.
3. Choose a set of algorithms: Once the preceding steps have been performed, it is possible to filter out the algorithms that perform well over the input data and that are able to arrive at the desired outcome. Depending on your resources and time limitations, you should choose from this list of apt algorithms the ones that you want to test out over your data problem, considering that it is always a good practice to try more than one algorithm.

These steps will be explained in more detail in the next chapter using a real-life data problem as an example.

Scikit-Learn

Created in 2007 by David Cournapeau as part of a Google Summer of Code project, scikit-learn is an open source Python library made to facilitate the process of building models based on built-in ML and statistical algorithms, without the need for hardcoding. The main reasons for its popular use are its complete documentation, its easy-to-use API, and the many collaborators who work every day to improve the library.

Note

You can find the documentation for scikit-learn at http://scikit-learn.org.

Scikit-learn is mainly used to model data, and not as much to manipulate or summarize data. It offers its us...