Since the concept of the dataset is essential in ML, let's look at it in detail, with a focus on how to create the required splits for building a complete and correct ML pipeline.

A dataset is nothing more than a collection of data. Formally, we can describe a dataset as a set of pairs, , where is the i-th example and is its label, with a finite cardinality, :

A dataset has a finite number of elements, and our ML algorithm will loop over this dataset several times, trying to understand the data structure, until it solves the task it is asked to address. As shown in Chapter 2, Neural Networks and Deep Learning, some algorithms will consider all the data at once, while other algorithms will iteratively look at a small subset of the data at each training iteration.

A typical supervised learning task is the classification of the dataset. We train...

Supervised learning algorithms work by extracting knowledge from a knowledge base (KB), that is, the dataset that contains labeled instances of the concept we need to learn about.

Supervised learning algorithms are two-phase algorithms. Given a supervised learning problem—let's say, a classification problem—the algorithm tries to solve it during the first phase, called the training phase, and its performance is measured in the second phase, called the testing phase.

The three dataset splits (train, validation, and test), as defined in the previous section, and the two-phase algorithm should sound an alarm: why do we have a two-phase algorithm and three dataset splits?

Because the first phase (should—in a well-made pipeline) uses two datasets. In fact, we can define the stages:

• Training and validation: The algorithm analyzes the dataset...

In comparison to supervised learning, unsupervised learning does not need a dataset of labeled examples during the training phase–labels are only needed during the testing phase when we want to evaluate the performance of the model.

The purpose of unsupervised learning is to discover natural partitions in the training set. What does this mean? Think about the MNIST dataset—it has 10 classes, and we know this because every example has a different label in the [1,10] range. An unsupervised learning algorithm has to discover that there are 10 different objects inside the dataset and does this by looking at the examples without prior knowledge of the label.

It is clear that unsupervised learning algorithms are challenging compared to supervised learning ones since they cannot rely on the label's information, but they have to discover features...

Semi-supervised learning algorithms fall between supervised and unsupervised learning algorithms.

They rely upon the assumption that we can exploit the information of the labeled data to improve the result of unsupervised learning algorithms and vice versa.

Being able to use semi-supervised learning algorithms depends on the available data: if we have only labeled data, we can use supervised learning; if we don't have any labeled data, we must go with unsupervised learning methods. However, let's say we have the following:

• Labeled and unlabeled examples
• Examples that are all labeled with the same class

If we have these, then we can use a semi-supervised approach to solve the problem.

The scenario in which we have all the examples labeled with the same class could look like a supervised learning problem, but it isn't.

If the aim of the...

In this chapter, we went through the ML algorithm families from a general and theoretical point of view. It is essential to have good knowledge of what machine learning is, how algorithms are categorized, what kind of algorithms are used given a certain task, and how to become familiar with all the concepts and the terminology that's used among machine learning practitioners.

In the next chapter, Chapter 2, Neural Networks and Deep Learning, we will focus on neural networks. We will understand the strengths of machine learning models, how is it possible to make a network learn, and how, in practice, a model parameter update is performed.

Answering the following questions is of extreme importance: you are building your ML foundations—do not skip this step!

1. Given a dataset of 1,000 labeled examples, what do you have to do if you want to measure the performance of a supervised learning algorithm during the training, validation, and test phases, while using accuracy as the unique metric?
2. What is the difference between supervised and unsupervised learning?
3. What is the difference between precision and recall?
4. A model in a high-recall regime produces more or less false positives than a model in a low recall regime?
5. Can the confusion matrix only be used in a binary classification problem? If not, how can we use it in a multiclass classification problem?
6. Is one-class classification a supervised learning problem? If yes, why? If no, why?
7. If a binary classifier has an AUC of 0.5, what can you conclude from...