Hlo🙏. The previous article in this series is about the Introduction to Machine Learning. If you had not still read it. Just go through it once. Now I am going to the next topic i.e. Types of machine learning algorithms. These algorithms are widely used in real-world scenarios.

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

4. Transfer Learning

1. Supervised Learning

Labelled data is given to train i.e. dataset with features(input) and along with the labels(output). Let us take the example of translation English speech can be converted to Telugu speech. The key characteristic is this model is trained before with different inputs and outputs. We are going to predict the right output from the given data. Here It can easily translate any language speech. Other examples are spam filtering, ads, and automated machines.

There are two types of the supervised machine learning

1. Classification

2. Regression

2. Unsupervised Learning

Unsupervised learning is also called descriptive analytics. It doesn’t have labelled data to train these algorithms. Training using unlabelled data is called Unsupervised learning. From given data, it discovers patterns that help solve clustering or association problems.

Example: News Section by google news, Computer Vision

There are three types of unsupervised learning

1. Clustering

2. Dimensionality Reduction

3. Anomaly Detection

The other types of machine learning algorithms which are quite popular in the modern world are Recommender Systems and Reinforcement Learning.

3. Reinforcement Learning

Reinforcement Learning is based on reward function.

4. Transfer Learning

It is like taking the pre-trained machine learning model and applying it to a similar type of problem that model is pre-trained on.

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