According to Arthur Samuel (https://en.wikipedia.org/wiki/Arthur_Samuel), data science gives computers the ability to learn without being explicitly programmed. So, any piece of software that will consume training examples in order to make decisions over unseen data without explicit programming is considered learning. Data science or learning comes in three different forms.

Figure 1.12 shows the commonly used types of data science/machine learning:

The majority of data scientists use supervised learning. Supervised learning is where you have some explanatory features, which are called input variables (X), and you have the labels that are associated with the training samples, which are called output variables (Y). The objective of any supervised learning algorithm is to learn the mapping function from the input variables (X) to the output variables (Y):

So the supervised learning algorithm will try to learn approximately the mapping from the input variables (X) to the output variables (Y), such that it can be used later to predict the Y values of an unseen sample.

Figure 1.13 shows a typical workflow for any supervised data science system:

This kind of learning is called supervised learning because you are getting the label/output of each training sample associated with it. In this case, we can say that the learning process is supervised by a supervisor. The algorithm makes decisions on the training samples and is corrected by the supervisor, based on the correct labels of the data. The learning process will stop when the supervised learning algorithm achieves an acceptable level of accuracy.

Supervised learning tasks come in two different forms; regression and classification:

• Classification: A classification task is when the label or the output variable is a category, such as tuna or Opah or spam and non spam
• Regression: A regression task is when the output variable is a real value, such as house prices or height

Unsupervised learning is viewed as the second most common kind of learning that is utilized by information researchers. In this type of learning, only the explanatory features or the input variables (X) are given, without any corresponding label or output variable.

The target of unsupervised learning algorithms is to take in the hidden structures and examples in the information. This kind of learning is called unsupervised in light of the fact that there aren't marks related with the training samples. So it's a learning procedure without corrections, and the algorithm will attempt to find the basic structure on its own.

Unsupervised learning can be further broken into two forms—clustering and association tasks:

• Clustering: A clustering task is where you want to discover similar groups of training samples and group them together, such as grouping documents by topic
• Association: An association rule learning task is where you want to discover some rules that describe the relationships in your training samples, such as people who watch movie X also tend to watch movie Y

Figure 1.14 shows a trivial example of unsupervised learning where we have got scattered documents and we are trying to group similar ones together:

Semi-supervised learning is a type of learning that sits in between supervised and unsupervised learning, where you have got training examples with input variables (X), but only some of them are labeled/tagged with the output variable (Y).

A good example of this type of learning is Flickr (https://www.flickr.com/), where you have got lots of images uploaded by users but only some of them are labeled (such as sunset, ocean, and dog) and the rest are unlabeled.

To solve the tasks that fall into this type of learning, you can use one of the following or a combination of them:

• Supervised learning: Learn/train the learning algorithm to give predictions about the unlabeled data and then feed the entire training samples back to learn from it and predict the unseen data
• Unsupervised learning: Use the unsupervised learning algorithms to learn the underlying structure of the explanatory features or the input variables as if you don't have any tagged training samples

The last type of learning in machine learning is reinforcement learning, in which there's no supervisor but only a reward signal.

So the reinforcement learning algorithm will try to make a decision and then a reward signal will be there to tell whether this decision is right or wrong. Also, this supervision feedback or reward signal may not come instantaneously but get delayed for a few steps. For example, the algorithm will take a decision now, but only after many steps will the reward signal tell whether decision was good or bad.