A machine learning system is fed with input data—this can be numerical, textual, visual, or audiovisual. The system usually has an output—this can be a floating-point number, for instance, the acceleration of a self-driving car, or can be an integer representing a category (also called a class), for example, a cat or tiger from image recognition.

The main task of machine learning is to explore and construct algorithms that can learn from historical data and make predictions on new input data. For a data-driven solution, we need to define (or have it defined to us by an algorithm) an evaluation function called loss or cost function, which measures how well the models are learning. In this setup, we create an optimization problem with the goal of learning in the most efficient and effective way.

Depending on the nature of the learning data, machine learning tasks can be broadly classified into the following three categories:

• Unsupervised learning: When the learning data only contains indicative signals without any description attached, it's up to us to find the structure of the data underneath, to discover hidden information, or to determine how to describe the data. This kind of learning data is called unlabeled data. Unsupervised learning can be used to detect anomalies, such as fraud or defective equipment, or to group customers with similar online behaviors for a marketing campaign.
• Supervised learning: When learning data comes with a description, targets, or desired output besides indicative signals, the learning goal becomes to find a general rule that maps input to output. This kind of learning data is called labeled data. The learned rule is then used to label new data with unknown output. The labels are usually provided by event-logging systems and human experts. Besides, if it's feasible, they may also be produced by members of the public, through crowd-sourcing, for instance. Supervised learning is commonly used in daily applications, such as face and speech recognition, products or movie recommendations, and sales forecasting.

We can further subdivide supervised learning into regression and classification. Regression trains on and predicts continuous-valued response, for example, predicting house prices, while classification attempts to find the appropriate class label, such as analyzing a positive/negative sentiment and prediction loan default.

If not all learning samples are labeled, but some are, we'll have semi-supervised learning. It makes use of unlabeled data (typically a large amount) for training, besides a small amount of labeled data. Semi-supervised learning is applied in cases where it's expensive to acquire a fully labeled dataset and more practical to label a small subset. For example, it often requires skilled experts to label hyperspectral remote sensing images and lots of field experiments to locate oil at a particular location, while acquiring unlabeled data is relatively easy.

• Reinforcement learning: Learning data provides feedback so that the system adapts to dynamic conditions in order to achieve a certain goal in the end. The system evaluates its performance based on the feedback responses and reacts accordingly. The best known instances include self-driving cars and the chess master, AlphaGo.

The following diagram depicts types of machine learning tasks:

Feeling a little bit confused by the abstract concepts? Don't worry. We'll encounter many concrete examples of these types of machine learning tasks later in this book. In Chapter 2, Exploring the 20 Newsgroups Dataset with Text Analysis Techniques, and Chapter 3, Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms, we'll explore unsupervised techniques and algorithms; in Chapter 4, Detecting Spam Email with Naive Bayes, and Chapter 8, Scaling Up Prediction to Terabyte Click Logs, we'll work on supervised learning tasks and several classification algorithms; in Chapter 9, Stock Price Prediction with Regression Algorithms, we'll continue with another supervised learning task, regression, and assorted regression algorithms.