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Machine Learning and its types



Machine Learning and its types: In this article, we are going to study about the different forms of a machine learning Problem. From this article onwards, we are going to dive deeper into ML concepts. This article will be followed by another upcoming article leading to its example problems for your better understanding. For the convenience few images have been downloaded from Google image section. For now, let’s just start, cause now is the time to take flight with me. Feel Free to ask your doubts in comment section.
Submitted by Atul Anand, on June 25, 2018

Machine Learning

Machine learning is some sort of statistical tools and algorithm that are used to learn from data. It is used to make machines capable of doing tasks like human intelligence.

Like a nascent human being, a machine learning model learns from data samples (predictor variable) as in training phase; and then, it finally concludes with predicting some answer (target).

But the nature of target variable may be a factor leading to broad classification of machine learning itself. Based on the nature of predictions we are going to make; our complete approach and method of solving the ML problem may vary from one to another.

  • Though this classification itself has some controversies. Few intellectuals classify it into 2 - subdivisions; few classify into 3 - subdivisions, and few into 4 - subdivisions.
  • Here, I am putting it broadly into 2- subdivisions for convenience; but, I will explain each of these 4 - subclasses.

Main types of machine learning

  1. Supervised Machine learning
  2. Unsupervised Machine Learning

Other Types

  • Semi - supervised Machine Learning
  • Reinforcement Learning (Mostly considered as supervised learning - Source Wikipedia)

1) Supervised Learning

The target variable/output is labeled. Viz. we are already aware of what we are going to Predict.

Points:

  • Uses data that is labeled
  • Who labels data? → Data expert (viz. oracle)
  • It also has two types: Classification and Regression. (See diagram below)
Machine Learning and its types


2) Unsupervised Learning

The target variable/output is not labeled. Viz. we are not known about the classes we are going to predict.

For solving this, we do grouping of data; and then, name them according to our Convenience or some expert results. This group is called "cluster" and method is known as "clustering".

POINTS

  • Uses data that is unlabelled.
  • Who labels data? → User doing predictions or may be data experts (after clustering).

This could be better understood with the help of a classification tree graph. I am also mentioning few popular and notable algorithms falling under these categories. But, these are just a few. (Keep updated to learn about others in detail).

Machine Learning and its types 2

Here, I am adding few more useful algorithms into this classification tree, and making it more general:

Machine Learning and its types 3


OTHERS

3) Semi-supervised Learning

Machine learning problems fall into this category when, we have a very few labeled data; and most of the target variable are unlabelled. We take help of those few labeled targets to decide classes for those unlabelled targets.

We first classify/predict with labelled target variables and consider unlabelled targets also. Let’s understand this through a diagram below:

Machine Learning and its types 4

Let’s conclude them with a very illustrative diagram:

Machine Learning and its types 5

4) Reinforcement Learning

It is based on scores. Its main objective is to find which actions should be taken in order to maximize rewards under a given setting. Each time we assign scores to each move in a winning game (say chess → computer v/s human) based on the result and the current state on the board. Each time we assign a grade to an action in order to minimize a punishment and/or maximize rewards.

Machine Learning and its types 6

Conclusion

Finally, I would conclude that ML problems are really fun while solving. Our approach towards solving a particular AI problem changes slightly depending on the type of problem. So, at first we need to recognize which kind of problem we are going to tackle. Then we should map out our approach. Obtain scores for different models; and then compile the optimized solution. This is path to solve any ML/AI problem. It makes your task look easy and fun. So, just figure out some Problems and find out by yourselves about their type. Feel free to ask your queries in comment section. Meet you in the next article. HAPPY LEARNING!






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