Types of Machine Learning You Should Know

4 

1. Supervised Learning - Teach a Machine to Learn

The most common type of machine learning where the computer is taught to recognize patterns in labeled data and can make predictions on new, unlabeled data.

Curious about its pros and cons?

Application of Supervised Learning  - Fraud detection   - Face recognition   - Speech Recognition

Explore each application in depth

2. Unsupervised Learning - Discover Hidden Patterns

In unsupervised learning, the computer is given a set of unlabeled data and is expected to find patterns and relationships on its own, without any guidance.

Curious about its pros and cons?

Applications of Unsupervised Learning  - Recommendation System  - Network Analysis

Explore each application in depth

3. Semi Supervised Learning - A Mix of Both Worlds

Semi-supervised learning is a hybrid approach that combines the best of supervised and unsupervised learning by training the computer on both labeled and unlabeled data.

Curious about its pros and cons?

Applications of Semi-Supervised Learning    - Text document classifier

Explore each application in depth

A type of ML where the computer learns by trial & error, receiving feedback in the form of rewards or punishments for its actions until it reaches an optimal decision-making strategy.

4. Reinforcement Learning - Learn by Trial and Error

Curious about its pros and cons?

Applications of Reinforcement Learning    - Robotics   - Video games

Explore each application in depth

Looking to take the plunge into the world of machine learning? 

InterviewBit has got you covered! Learn all types of ML, including their pros/cons & real-world applications & become an ML expert in no time.

Step Up Your Game with InterviewBit Web Stories

Don't miss out on the chance to upskill yourself with IntervewBit's engaging web stories. 

“