Different Types of Machine Learning

A Quick Overview 

ML is a form of artificial intelligence that makes predictions based on data. It enables computers or machines to make decisions automatically without being explicitly programmed.

Introduction to Machine Learning

ML can be broadly divided into 4 types:   1. Supervised Machine Learning  2. Unsupervised Machine Learning  3. Semi-Supervised Machine Learning  4. Reinforcement Learning

Types of Machine Learning

1. Supervised Machine Learning

It involves training machines using well-labeled training data, and then machines predict output using that data. Labeled data means input data has already been tagged with correct output.

2. Unsupervised Machine Learning

It involves training machines using unlabelled datasets, and then machine predicts outcome (without supervision). The machine is trained to recognize patterns of objects.

3. Semi-Supervised Machine Learning

It involves training machines using both labeled and unlabelled datasets. Therefore, it serves as an intermediate layer between supervised and unsupervised learning.

4. Reinforcement Learning

A third-party agent (software) trains the model with hit-and-trial and improves its performance by learning from mistakes. Thus, it improves the machine's performance.

Types of Reinforcement Learning

1. Positive:  It occurs when an event takes place because of a particular behavior, which increases its strength and frequency and leads to +ve impact on behavior.

2. Negative:  Negative reinforcement involves removing a negative condition to strengthen behavior. It improves the specific behavior

Explore ML Types in detail with their Applications.

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