This article is about Machine Learning
An Introduction to Machine Learning in Simple English
By NIIT Editorial
Published on 11/12/2020
Machine learning has been the catchphrase on the tongue-tips of many thanks to a multi-fold increase in its applications. Whether it is a client-facing or an enterprise app, they all collect data, which is the non-replaceable feed that powers ML-algorithms. But let us not get ahead of ourselves. Let us understand the basics behind machine learning and its algorithms.
What is Machine Learning?
It is a domain of artificial intelligence wherein computers learn to be experiential in their approach to process and learn from data, without being programmed to do so. Feeding on large chunks of information, machine learning algorithms are capable to mark out patterns and learn from them. The objective of machine learning is to automate as many operations as possible. The workflow for a machine learning program can be summarized as follows:
- The data is inputted in the algorithm along with defining the tags used to label and study the data.
- Data morphs into vectors.
- The algorithm studies the data based on the defined tags, and upon acquiring a minimum threshold of understanding begins to make predictions.
Machine learning is where AI gets its autonomous form factor from. The training data, used as the feed for machine learning algorithms, teach the computer to progress so there is no need for algorithm developers to outrightly code for the operation.
Machine Learning Algorithms
To understand the nature of machine learning, we need to look upon how and what kind of algorithms are set in motion by the developers. There are 4 categories used to classify the algorithms:
Such algorithms calculate projections based on the tags marked on the initial training set. First, the algorithms are introduced to the nature of the input and the expected output. They are called supervised because the tags have to be manually fed into the system. An example could be, teaching the algorithm to identify dogs. For the same, it can be fed thousands of dog images with characteristic features such as ear-shape, nose, whisker length, etc. tagged accordingly.
The purpose of these algorithms is to reveal the analysis of unlabeled data. Developers can define the input, but the output is something that they are unsure of. The code is at liberty to reveal its findings without any limitations. Unsupervised learning is often the option data scientists turn to when they want to undertake the exploratory analysis. An example of this could be customer segmentation where the available customer data is simply tagged based on their demographics. Machine learning can be used to reveal hidden buying habits from thereon.
This is a step ahead of supervised learning models. The sample data is distributed into two fragments. One is a labelled set and the second is unlabelled. The labelled set is usually smaller in size than the unlabeled data. The former is inputted into the algorithm for training. The algorithm uses this as a starting point to identify related patterns from unlabelled data. A growing sentiment within the industry prefers this approach over supervised learning as it is much cost-efficient and doable with fewer data. Not to mention, the results are more accurate.
Reinforcement models are aimed at maximising the rewards of the outcome. The software is designed to recommend the best resource utilisation methods. Instead of training the algorithm through data, it is allowed to learn through a hit and trial method. Game developers are familiar with this approach as the system is dynamic and results dependent on the best course of action.
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