Machine Learning: What, Why, and How?

  • Salma Jamal
  • Sukriti Goyal
  • Abhinav Grover
  • Asheesh Shanker


Since the beginning of computers era, an enormous amount of data generated seems to be ever-increasing and could be of great use with efficient learning techniques. Learning from the data to make reliable predictions, discovering new patterns and theories has been the most challenging task for the researchers. Machine learning detects the hidden insights in the data, learns from them, and makes reliable predictions on the unseen data. It is used in a range of applications that include bioinformatics, cheminformatics, marketing, linguistics, email filtering, optical character recognition, and many more. In the present chapter, we have described the types of learning, applications of machine learning, steps to generate machine learning models using various learning algorithms, and validation of generated models. Additionally, step-by-step generation of models using Weka workbench which is a collection of machine learning algorithms and data preprocessing tools has also been discussed.


Machine learning Cheminformatics Weka Feature selection Classification 



Salma Jamal acknowledges a Senior Research Fellowship from the Indian Council of Medical Research (ICMR).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Salma Jamal
    • 1
  • Sukriti Goyal
    • 1
  • Abhinav Grover
    • 2
  • Asheesh Shanker
    • 1
    • 3
  1. 1.Department of Bioscience and BiotechnologyBanasthali VidyapithRajasthanIndia
  2. 2.School of BiotechnologyJawaharlal Nehru UniversityNew DelhiIndia
  3. 3.Department of BioinformaticsCentral University of South BiharGayaIndia

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