Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this article is to introduce machine learning


What is Machine Learning?


Machine Learning (ML). we wish to program in computers so that they can “learn” from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge.

Suppose program a machine that learns how to filter spam e-mails from email box. The machine will simply memorize all previous e-mails that had been labeled as a spam e-mails by the user (human being). When a new e-mail come to, then the machine will search automatically for it in the set of previous spam e-mails folder. If it matches one of them, it will be trashed. Otherwise, it will be moved to the user’s inbox folder.


Machine Learning


>> machine are capable to “learn” from “data” or “past experience”


  • data: May be comes from various sources such as domain knowledge, sensors, experimental runs, etc.


  • Learn: Become intelligent predictions or decisions based on data


  1.  Supervised learning:

Example:- decision trees, neural networks, etc.

  1. Unsupervised learning:

Example:-   k-means clustering, etc.

3. Reinforcement learning


Supervised Learning


  • Given a labeled set of input-output pairs, objective is to learn a function mapping the inputs to outputs
  • Inputs can be complex objects such as images, sentences, speech signals, etc. Typically take the form of features.
  • Outputs are either categorical (classification tasks) or real-valued (regression tasks). More on these concepts in later classes.

Unsupervised Learning

  • Given a set of inputs, , discover some patterns in the data
  • Most common example: Clustering


Reinforcement learning

A master chess player makes a move in chess game. The choice is informed both by planning—expect

possible replies and counterreplies—and by instant, intuitive judgments of the desirability of

particular positions and moves..


Need of Machine Learning?

  • For tasks that are easily performed by humans but are complex for computer systems to emulate
  • Vision:Identify faces in a photograph, objects in a video or still image, etc.
  • Natural language:Translate a sentence from Hindi to English, question answering, etc.
  • Speech: Recognise spoken words, speaking sentences naturally
  • Game playing:Play games like chess
  • Robotics: Walking, jumping, displaying emotions, etc.
  • Driving a car, flying a plane, navigating a maze, etc


  • For tasks that are beyond human capabilities
  • Analysis of large and complex datasets
  • E.g. Autopilot controls



common algorithm of Machine Learning


  • Supervised learning


  • Unsupervised learning


  • Semi-supervised learning


  • Reinforcement learning


  • Transduction


  • Learning to learn



If you want to study Machine Learning you have to knowledge of


  • probability


  • linear algebra


  • multivariable calculus


  • Programming