This chapter introduces the basics of machine learning, laying down the common themes and concepts and making it easy to follow the logic and familiarize yourself with the topic. The goal is to quickly learn the step-by-step process of applied machine learning and grasp the main machine learning principles. In this chapter, we will cover the following topics:
- Introducing machine learning and its relation to data science
- Discussing the basic steps in applied machine learning
- Discussing the kind of data we are dealing with and its importance
- Discussing approaches of collecting and preprocessing the data
- Making sense of data using machine learning
- Using machine learning to extract insights from data and build predictors
If you are already familiar with machine learning and are eager to start coding, then quickly jump to the following chapters. However, if you need to refresh your memory or clarify some concepts, then it is strongly recommend to revisit the topics presented in this chapter.
Nowadays, everyone talks about machine learning and data science. So, what exactly is machine learning anyway? How does it relate to data science? These two terms are commonly confused, as they often employ the same methods and overlap significantly. Therefore, let's first clarify what they are.
Josh Wills tweeted:
"Data scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician". | ||
| --(Josh Wills) | ||
More specifically, data science encompasses the entire process of obtaining knowledge from data by integrating methods from statistics, computer science, and other fields to gain insight from data. In practice, data science encompasses an iterative process of data harvesting, cleaning, analysis and visualization, and deployment.
Machine learning, on the other hand, is mainly concerned with fairly generic algorithms and techniques that are used in analysis and modeling phases of data science process. Arthur Samuel proposed the following definition back in 1995:
"Machine Learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed." | ||
| --Arthur Samuel | ||
Among the different machine learning approaches, there are three main ways of learning, as shown in the following list:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Given a set of example inputs, X, and their outcomes, Y, supervised learning aims to learn a general mapping function, f, that transforms inputs to outputs, as f: X Y
An example of supervised learning is credit card fraud detection, where the learning algorithm is presented with credit card transactions (matrix X) marked as normal or suspicious. (vector Y). The learning algorithm produces a decision model that marks unseen transactions as normal or suspicious (that is the f function).
In contrast, unsupervised learning algorithms do not assume given outcome labels, Y as they focus on learning the structure of the data, such as grouping similar inputs into clusters. Unsupervised learning can, hence, discover hidden patterns in the data. An example of unsupervised learning is an item-based recommendation system, where the learning algorithm discovers similar items bought together, for example, people who bought book A also bought book B.
Reinforcement learning addresses the learning process from a completely different angle. It assumes that an agent, which can be a robot, bot, or computer program, interacts with a dynamic environment to achieve a specific goal. The environment is described with a set of states and the agent can take different actions to move from one state to another. Some states are marked as goal states and if the agent achieves this state, it receives a large reward. In other states, the reward is smaller, non-existing, or even negative. The goal of reinforcement learning is to find an optimal policy, that is, a mapping function that specifies the action to take in each of the states without a teacher explicitly telling whether this leads to the goal state or not. An example of reinforcement learning is a program for driving a vehicle, where the states correspond to the driving conditions—for example, current speed, road segment information, surrounding traffic, speed limits, and obstacles on the road—and the actions can be driving maneuvers such as turn left or right, stop, accelerate, and continue. The learning algorithm produces a policy that specifies the action that is to be taken in specific configuration of driving conditions.
In this book, we will focus on supervised and unsupervised learning only, as they share many concepts. If reinforcement learning sparked your interest, a good book to start with is Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew Barto.
This book's emphasis is on applied machine learning. We want to provide you with the practical skills needed to get learning algorithms to work in different settings. Instead of math and theory of machine learning, we will spend more time on the practical, hands-on skills (and dirty tricks) to get this stuff to work well on an application. We will focus on supervised and unsupervised machine learning and cover the essential steps from data science to build the applied machine learning workflow.
A typical workflow in applied machine learning applications consists of answering a series of questions that can be summarized in the following five steps:
- Data and problem definition: The first step is to ask interesting questions. What is the problem you are trying solve? Why is it important? Which format of result answers your question? Is this a simple yes/no answer? Do you need to pick one of the available questions?
- Data collection: Once you have a problem to tackle, you will need the data. Ask yourself what kind of data will help you answer the question. Can you get the data from the available sources? Will you have to combine multiple sources? Do you have to generate the data? Are there any sampling biases? How much data will be required?
- Data preprocessing: The first data preprocessing task is data cleaning. For example, filling missing values, smoothing noisy data, removing outliers, and resolving consistencies. This is usually followed by integration of multiple data sources and data transformation to a specific range (normalization), to value bins (discretized intervals), and to reduce the number of dimensions.
- Data analysis and modeling with unsupervised and supervised learning: Data analysis and modeling includes unsupervised and supervised machine learning, statistical inference, and prediction. A wide variety of machine learning algorithms are available, including k-nearest neighbors, naïve Bayes, decision trees, support vector machines, logistic regression, k-means, and so on. The choice of method to be deployed depends on the problem definition discussed in the first step and the type of collected data. The final product of this step is a model inferred from the data.
- Evaluation: The last step is devoted to model assessment. The main issue models built with machine learning face is how well they model the underlying data—if a model is too specific, that is, it overfits to the data used for training, it is quite possible that it will not perform well on a new data. The model can be too generic, meaning that it underfits the training data. For example, when asked how the weather is in California, it always answers sunny, which is indeed correct most of the time. However, such a model is not really useful for making valid predictions. The goal of this step is to correctly evaluate the model and make sure it will work on new data as well. Evaluation methods include separate test and train set, cross-validation, and leave-one-out validation.
In the following sections, we will take a closer look at each of the steps. We will try to understand the type of questions we must answer during the applied machine learning workflow and also look at the accompanying concepts of data analysis and evaluation.