Machine Learning algorithms can be broadly categorized into the following four groups:

  • Supervised Learning
  • Unsupervised Learning
  • Semisupervised Learning
  • Reinforcement Learning

1.Supervised Learning

In Supervised Learning, the training data provided as input to the algorithm includes the final solutions, called labels or class because the algorithm learns by “looking” at the examples with correct answers. In other words, the algorithm has a supervisor or a teacher who provides it with all the answers first, like whether it’s a cat in the picture or not. And the machine uses these examples to learn one by one. The spam filter is another good example of this.

Another typical task, of a different type would be to predict a target numeric value like housing prices from a set of features like size, location, number of bedrooms. To train the system, we again need to provide many correct examples of known housing prices, including both their features and their labels.

While categorizing emails or identifying whether the picture is of a cat or a dog was a supervised learning algorithm of type classification, predicting housing prices is known as regression. What’s the difference?

In regression the output is a continuous value or a decimal number like housing prices. In classification, the output is a label like “spam or not-spam” and not a decimal number; the output only takes values like 0 or 1 where we could have 1 for “spam” and 0 for “non-spam”. Basically, the type of algorithm we choose (classification or regression) depends on the type of output we want.

Examples of Supervised Learning Algorithms:

  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees and Random Forests
  • k-Nearest Neighbors
  • Neural networks

While the focus of this lesson is to learn about the broad categories, we will be diving deeper into each of these algorithms individually in the "Machine Learning Algorithms" lesson.

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