Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence.

The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data.

As such, there are many different types of learning that you may encounter as a practitioner in the field of machine learning: from whole fields of study to specific techniques.

In this post, you will discover a gentle introduction to the different types of learning that you may encounter in the field of machine learning.

After reading this post, you will know:

• Fields of study, such as supervised, unsupervised, and reinforcement learning.
• Hybrid types of learning, such as semi-supervised and self-supervised learning.
• Broad techniques, such as active, online, and transfer learning.

Let’s get started.

Types of Learning

Given that the focus of the field of machine learning is “learning,” there are many types that you may encounter as a practitioner.

Some types of learning describe whole subfields of the study comprised of many different types of algorithms such as “supervised learning.” Others describe powerful techniques that you can use on your projects, such as “transfer learning.”

There are perhaps 14 types of learning that you must be familiar with as a machine learning practitioner; they are:

Learning Problems

• 1. Supervised Learning
• 2. Unsupervised Learning
• 3. Reinforcement Learning

Hybrid Learning Problems

• 4. Semi-Supervised Learning
• 5. Self-Supervised Learning
• 6. Multi-Instance Learning

Statistical Inference

• 7. Inductive Learning
• 8. Deductive Inference
• 9. Transductive Learning

Learning Techniques

• 10. Multi-Task Learning
• 11. Active Learning
• 12. Online Learning
• 13. Transfer Learning
• 14. Ensemble Learning