Different Types of Machine Learning Techniques

Machine Learning is a subset of Artificial Intelligence that gives computers the capability to learn and improve automatically from data without being explicitly programmed.

Types of Machine Learning Techniques

In supervised learning, we train the model on a labelled dataset. A labelled dataset has both input and output parameters. So, we already know what our correct output should look like.

Types of Supervised Learning:

Classification: It’s a Supervised Learning task where the outputs are restricted to a limited set of values.

For example:

Regression: It’s a Supervised Learning task where the outputs are having continuous values.

For example:

In unsupervised learning, we train the model on a dataset that contains only input parameters. The model by itself finds structures in the dataset, like grouping or clustering of data points.

For example:

In Reinforcement Learning, the software agent interacts with its environment and keeps on increasing its performance using a reward feedback to learn the behavior or pattern.

For example: Google’s self driving car.

Fig 2: Unsupervised Learning: https://medium.com/the-21st-century/machine-learning-a-strategy-to-learn-and-understand-chapter-3-9daaad4afc55

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