Machine Learning Methods Explained

Machine Learning Methods Explained

Machine Learning Methods Explained. In this article, we will cover the methods that are most commonly used in machine learning projects (with examples).

In machine learning, every problem is a little different and requires a different approach. The problem can be a stock price prediction, classifying an image, detecting objects in an image, grouping the same type of data, and about a million others.

In this article, we will cover the methods that are most commonly used in machine learning projects (with examples).

See also: Best Tools to Manage Machine Learning Projects

Types of Learning

We can categorize the machine learning methods into 3 types of learning.

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

1. Supervised Learning

In supervised learning, we get the data with the target value. To put this into layman terms, say we have the data about a geographical location which consists of lot-size, house age, bedroom number and we want to purchase a house in that particular area.

Now we can write a mathematical function which will take X = {lot_size, house_age, bedroom_number} and will return the house price ‘y’.

Where X is a feature vector and y is a target vector.

This method is most often the best option but the downside is you need to have the access to y. In some projects, this is easy (and cheap) in others getting lots of labeled data can be very difficult (and expensive).

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