Machine learning

Let me define machine learning and its components so that you don't get bamboozled by lots of jargon when it gets thrown at you.

In the words of Tom Mitchell, "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." Also, another theory says that machine learning is the field that gives computers the ability to learn without being explicitly programmed.

For example, if a computer has been given cases such as, [(father, mother), (uncle, aunt), (brother, sisters)], based on this, it needs to find out (son, ?). That is, given son, what will be the associated item? To solve this problem, a computer program will go through the previous records and try to understand and learn the association and pattern out of these combinations as it hops from one record to another. This is called learning, and it takes place through algorithms. With more records, that is, more experience, the machine gets smarter and smarter.

Let's take a look at the different branches of machine learning, as indicated in the following diagram:

We will explain the preceding diagram as follows:

• Supervised learning: In this type of learning, both the input variables and output variables are known to us. Here, we are supposed to establish a relationship between the input variables and the output, and the learning will be based on that. There are two types of problems under it, as follows:
  • Regression problem: It has got a continuous output. For example, a housing price dataset wherein the price of the house needs to be predicted based on input variables such as area, region, city, number of rooms, and so on. The price to be predicted is a continuous variable.
  • Classification: It has got a discrete output. For example, the prediction that an employee would leave an organization or not, based on salary, gender, the number of members in their family, and so on.
• Unsupervised learning: In this type of scenario, there is no output variable. We are supposed to extract a pattern based on all the variables given. For example, the segmentation of customers based on age, gender, income, and so on.
• Reinforcement learning: This is an area of machine learning wherein suitable action is taken to maximize reward. For example, training a dog to catch a ball and give it—we reward the dog if they carry out this action; otherwise, we tell them off, leading to a punishment.