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Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI). 

Today almost all the industries are making benefits from machine learning including automobiles, health care, finance, etc. Machine learning helps these industries by automating procedures, reducing processing time, providing more accurate and faster decisions. It works by developing procedures that take input data and then by applying statistical analysis on the data, it predicts an output.

The term machine learning was coined in 1959 by Arthur Samuel, an American pioneer in the field of artificial intelligence and computer gaming. Artificial Intelligence (AI) is a broad area of science which performs simulates human abilities. Machine learning is a subset of AI. Machine learning provides ability to learn from experiences and brings computers more similar to humans.

How does Machine Learning works?

Machine learning algorithms are often categorized as supervised, unsupervised and reinforcement learning.

  1. Supervised machine learning algorithms works by receiving input data, desired output and feedback from data scientist/ data analyst and update the algorithms to improve the precision of predictions.
  2. Unsupervised machine learning algorithms require no prior training. They use deep learning, iterative approach for reviewing data and reaching conclusions. Furthermore, unsupervised learning algorithms using neural networks automatically find correlations between many variables and can solve even more complex processing tasks than that of supervised learning systems.
  3. Like unsupervised learning, reinforcement learning is not provided with input/output pairs but like supervised learning, feed-backs in the form of rewards or punishments are provided in reinforcement learning.

Steps in Machine Learning Process

  1. Identify relevant data sets and prepare them for analysis.
  2. Choose an appropriate machine learning algorithm to use.
  3. Build an analytical model on the chosen algorithm.
  4. Training of model on input data sets.
  5. Executing model to generate scores and results.

Requirements of good Machine Learning System

  • Abilities to prepare data
  • Advanced algorithms design
  • Automation and iterative processes
  • Scalability
  • Ensemble modeling

Impact of Machine Learning on Everyday Life

Today’s machine learning has given us self-driving cars, practical speech recognition, and effective web search and today we are actively using it many times a day even without knowing it. This is because of innovative computing technologies that complex mathematical calculations can be applied automatically to big data more quickly and repeatedly. Few broadly publicized examples of machine learning applications include neuroscience, Internet of Things, online shopping, spam filtering, intelligent game playing, etc.

References: Nasib S. Gill and D. Sehrawat, “Machine Learning and Analytics”, CSI, May 2019.

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