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Introduction to Machine learning

Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision by itself without human intervention. It describes how computer perform tasks on their own by previous experiences. Therefore we can say in machine language artificial intelligence is generated on the basis of experience.

The difference between normal computer software and machine learning is that a human developer hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a large number of data.

Uses of Machine Learning

Some of the machine learning algorithms are:
  • Neural Networks
  • Random Forests
  • Decision trees
  • Genetic algorithm
  • Radial basis function
  • Sigmoid

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Types of Machine Learning

There are three types of machine learning

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
Supervised learning

Supervised learning is a technique where the program is given labelled input data and the expected output data. It gets the data from training data containing sets of examples. They generate two kinds of results :

Classification: They notify the class of the data it is presented with.

Regression: they expect the product to produce a numerical value.


This type of algorithm consists of input data without labelled response. There will not be any pre existing labels and human intervention is also less. It is mostly used in exploratory analysis as it can automatically identify the structure in data.


This model is used in making a sequence of decisions. It is an learning by interacting with the environment. It is based on the observation that intelligent agents tend to repeat the action that are rewarded for and refrain from action that are punished for. It can be said that it is an trail and error method in finding the best outcome based on experience.

Machine Learning Uses:

  • Traffic prediction
  • Virtual Personal Assistant
  • Speech recognition
  • Email spam and malware filtering
  • Bioinformatics
  • Natural language processing

Real Time Examples for Machine Learning

Traffic prediction:

By using GPS navigation service out location are saved at the central server for managing traffic. Based on the number of gps tracked at the location traffic at the particular Street is identified.

Virtual Personal Assistant: 

Smart Speakers, Smartphones and apps like google allo.

Online Transportation:

In apps like uber the available vehicle near our area, the estimated cost and distance of the travel are computed using this technique.

Social Media Services:

In app like Facebook personalizing our news feed, people you may know are done using Machine learning.

Email spam filtering:

There are number of approaches clients use. These filters are continuously updated and powered by machine learning.

Product Recommendation:

In online shopping while we search for a product all its relavant products are displayed in our screen . It is based on the technique of machine learning.

Online Fraud detection:

Tracking monetary frauds online by making cyber space a secure place is an example of machine learning.

Best Programming Languages for Machine Learning:

Some of the best and most commonly used machine learning programs are

  • Python,
  • java,
  • C,
  • C++,
  • Shell,
  • R,
  • JavaScript,
  • Scala,
  • Shell,
  • Julia

Machine Learning vs Artificial Intelligence

Difference Between Machine Learning And Artificial Intelligence

Artificial Intelligence is a concept of creating intelligent machines that stimulates human behaviour whereas Machine learning is a subset of Artificial intelligence that allows machine to learn from data without being programmed.

Advantages of ML

  • Fast, Accurate, Efficient.
  • Automation of most applications.
  • Wide range of real life applications.
  • Enhanced cyber security and spam detection.
  • No human Intervention is needed.
  • Handling multi dimensional data.

Disadvantages of ML

  • It is very difficult to identify and rectify the errors.
  • Data Acquisition.
  • Interpretation of results Requires more time and space.
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