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Fundamentals of Machine Learning: A Pythonic Introduction

Types of Machine Learning

Learn the difference between the three main paradigms of learning (Supervised, Unsupervised, and Reinforcement) to choose the right approach for a problem.

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Machine learning (ML) is an expansive field, but at its heart we are teaching computers to learn from data without being explicitly programmed. Not all problems look the same, and the process we use to build a spam filter, which needs labeled examples of “spam” versus “not spam,” is fundamentally different from the process we use to organize millions of customer photos, which have no predefined labels. To successfully apply machine learning, we first need to answer a critical question: What type of data do we have, and what are we trying to achieve? In this lesson, we will introduce the three main learning paradigms, supervised, unsupervised, and reinforcement learning, so we can choose the correct approach for any given problem.

The role of data in machine learning

Data is the essential fuel that allows machine learning models to grasp generalizable patternsA generalizable pattern is a relationship or rule discovered by the model that holds true not just for the training data it learned from, but also for new, previously unseen data.. The specific data requirements depend entirely on the problem we are trying to solve.

For example, to build a facial recognition system, the required data consists of numerous face images and their corresponding identities (known as labels). The ultimate goal is to learn a mapping function that accurately associates each face image with its correct identity label. This process, where the output is known and provided to the model, is said to have supervisionIt refers to the presence of labeled data that enables a machine learning model to learn and improve its accuracy. characteristics.

Based on the type of data available and the nature of the learning problem, machine learning can be broadly categorized into three main types:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning

While all machine learning algorithms utilize data, supervised learning is considered the most data-intensive category.

A supervised learning algorithm takes labeled data as input and uses it to train a model that can ...