Machine Learning: A Beginner’s Guide

Definition: Machine Learning is basically the process of how a machine learns and takes decisions just like a human being. In other words, Machine Learning is getting a computer (machine) to program itself without any human intervention.
A Human observes different events, patterns and accordingly takes decisions. Similarly, a machine observes patterns, gains are given a given data set and then takes decision accordingly. The data set can be small, large or very large.
Machine Learning uses algorithms that analyze input data to predict the output values within an acceptable range. When new data is fed to these algorithms, they learn and improvise their operations in order to improve performance and develops intelligence over time.

Types of Machine learning Algorithms

Supervised Learning: The machine learning algorithm is provided with a known dataset which has the desired inputs and outputs and the algorithm must try to find a technique to determine how to arrive at those inputs and outputs.

Unsupervised Learning: In unsupervised learning, the machine itself has to discover interesting patterns and structures as unlike supervised learning there are no correct output values given.

Reinforcement Learning: In reinforcement learning the machine works in a given environment in a set of rules in order to achieve the best result possible by trial and error method. It learns from its past errors and adapts it to give better outcomes in its future approach. For example, if reinforcement learning is applied to a game then the machine will learn its success and failure by feedbacks from the game in terms of rewards and punishments.

Why do we need Machine Learning?

Due to the increasing variety and amount of data, we need faster, cheaper and more efficient computational techniques in order to complete tasks well before in time and to move forward in terms of technology. Thus, machine learning in today’s modern world being one of the most advanced computational methods is highly important to various industries, from private to public. Moreover, complex codes that are impractical to be programmed by humans also need the use of machine learning.

How is Machine Learning (ML) different from Artificial Intelligence (AI)?

Artificial Intelligence in simple words is nothing but the procedure of programming a machine in order to come out with a basis for the decision to be made i.e., it includes programs to see if certain parameters in a program are functioning normally.
Whereas Machine Learning is a subset of AI, where the machine is trained to learn from its past observations and experiences which is in turn collected from the provided data.

Applications of Machine Learning

Web Search – to rank the pages based on what the user is most likely to search and click on.

Finance – to decide credit card offers for specific individuals. For nvestment planning and evaluation of risk in credit schemes.

E-Commerce – To predict customer likes, most likely products to buy, detect fraud transactions.

Robotics – To learn about the obstacles and other uncertainties in environment around.

Social Networking – To extract value from various relevant data on social networking sites such as likes/relationships etc.

Debugging – detecting bugs in complex codes, major use in computer science.

Comments

  • Kalpalathika
    January 16, 2019

    Very informative.

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