Every company can be an artificial intelligence (AI) company. This article touches upon the basics of ML and how Automatic ML is making AI accessible to a wider community of people. This article is based on a webinar titled –Introduction to Machine Learning for All of Us,conducted by Rafael Coss, Director of Technical Marketing atH2O.ai
According to Wikipedia, AI is the study of “intelligent agents” i.e any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. In order words, Artificial intelligence is a field of computer science that provides the ability for a computer to learn and reason like humans using several available techniques.
The field of AI has evolved over the years and is currently making a lot of progress. The possible reasons for its success today are:
So the fact that these three things have been commoditized is a key enabler to make AI a reality today. And that’s why in 2020, AI is spreading like wildfire through various enterprises.
Machine Learning, on the other hand, is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.
Most recent advances in AI have been achieved by applying machine learning to very large data sets. With big data and the digitalization of the world, more and more data is becoming available to us. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instructions.
Machine Learning is today being applied to a number of use cases, for example:
Depending upon these use cases, machine learning can be broadly classified as:
During Supervised learning, a computer learns by example. A Supervised learning algorithm takes a known set of input data(training examples) and the corresponding responses to the data (label) and trains a model to generate reasonable predictions for the response to new data(test data).
Let’s look at an example wherein a bank is trying to use machine learning to figure out if someone’s going to default on a loan or not. The dataset provided is of a credit card transaction of the person for the last year. Since the idea is to find a category i.ewill defaultorwon’t default,this is a classic case of Supervised learning classification.
Algorithms like Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines etc are used for Supervised learning.
Unsupervised learning, on the other hand, is a type ofmachine learningthat looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. This means, unlike supervised learning, we do not have a variable to predict; instead we try to uncover hidden patterns in data so that we can identify clusters or groups within data. An example wherein a company wants to segment customers into groups by distinct characteristics like age, income group to better understand its customers is a use case of Unsupervised learning.
Algorithms like K-Means clustering are used for Unsupervised learning.
AI-backed technologies are being used across a variety of different industries including but not limited to finance, healthcare, telecom, marketing and retail, IoT, manufacturing, etc. The use of AI in the industry is quickly changing the business landscape, even in traditionally conservative areas.
