Machine Learning Algorithms Explained -By Saurabh

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Welcome back on our  journey to learn the basics of Machine learning.Before having a hands on on real time data I thought making you all familiar with various types of machine learning algorithms.Let as now breakdown the classification of Machine Learning. 




Let us go through each type one by one.

1. Supervised Learning : 
Imagine you are a student in a class.Teacher trains you by giving you knowledge about a subject. Supervised learning is like learning from a teacher here the machine is you(student) and the teacher is the training data(the data we discussed in the previous blog,one stored in the excel file).Training data will be used to train the model.
Supervised Learning is further divided into two types:- 
  •  Regression
  • Classification

Regression includes a scenario where machine is trained to predict some value like price ,height and weight other example will be  predict stock market prices etc.Algorithms which can perform regression are Linear Regression,Decision Tree, SVM,KNN

Classification is where machine is trained to classify something into some class.Examples include classifying whether a patient is diseased or not . Simple example will be a student made to classify between apples and oranges. Algorithms which can perform classification are Logistic Regression,Decision Tree,etc


 2.Unsupervised Learning :- Unsupervised Learning is like learning  without a teacher.The machine(You) learns through observations and find structures in data.
Example :-
Clustering -  A clustering problem is where you have to discover groups in the data such as grouping customers based on their purchasing behavior according to their annual income.A glance at the following graph will make things clear.




3.Reinforcement Learning:- Reinforcement learning is like learning from your mistakes. Lets take an example of small boy roaming in a house , being a small boy he roams from room to room running into things ,falling and getting up again and at a point the child enters the kitchen and gets hurt by touching a hot pressure cooker just taken off gas.He cries and his parents fetch up his burn.

He now comes to now that touching a hot pressure cooker will get him hurt and the next time he has avoid it , the same goes with reinforcement learning .It learns from its mistakes and improves on. 
Algorithm example:- Markov Decision Process and Q learning. 
    



Thanks for Reading!!!


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