Machine learning mimicking human intelligence is a subfield of artificial intelligence—a field of computer science concerned with creating systems. Software engineering is another field in computer science. Generally, we can label Python programming as a type of software engineering. Machine learning is also closely related to linear algebra, probability theory, statistics, and mathematical optimization. We usually build machine learning models based on statistics, probability theory, and linear algebra, then optimize the models using mathematical optimization. The majority of us reading this book should have at least sufficient knowledge of Python programming. Those who are not feeling confident about mathematical knowledge, might be wondering, how much time should be spent learning or brushing up the knowledge of the aforementioned subjects. Don't panic. We will get machine learning to work for us without going into any mathematical details in this book. It just requires some basic, 101 knowledge of probability theory and linear algebra, which helps us understand the mechanics of machine learning techniques and algorithms. And it gets easier as we will be building models both from scratch and with popular packages in Python, a language we like and are familiar with.

A machine learning system is fed with input data—this can be numerical, textual, visual, or audiovisual. The system usually has outputs—this can be a floating-point number, for instance, the acceleration of a self-driving car, 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 for 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 three categories as follows:

Feeling a little bit confused by the abstract concepts? Don't worry. We will encounter many concrete examples of these types of machine learning tasks later in the book. In Chapter 3, Spam Email Detection with Naive Bayes, to Chapter 6, Click-Through Prediction with Logistic Regression, we will see some supervised learning tasks and several classification algorithms; in Chapter 7, Stock Price Prediction with Regression Algorithms, we will continue with another supervised learning task, regression, and assorted regression algorithms; while in Chapter 2, Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms, we will be given an unsupervised task and explore various unsupervised techniques and algorithms.