What is Machine Learning and Why it matters to the world
Artificial Intelligence, Machine Learning, Deep learning are three of the hottest buzzwords in the industry today. And often, we tend to use the terms Artificial Intelligence (AI) and Machine Learning (ML) synonymously. However, these two terms are very different – machine learning is one among the crucial aspects of the much broader field of AI.
Nidhi Chappell, the Head of ML at Intel puts it down aptly:
“AI is basically the intelligence – how we make machines intelligent, while machine learning is the implementation of the compute methods that support it. The way I think of it is: AI is the science and machine learning is the algorithms that make the machines smarter.”
Thus, to put it in simple words, AI is a field that involves in making machines into “intelligent and smart” units, whereas ML is a branch under artificial intelligence that deals in teaching the computer to “learn” to perform tasks on its own.
The Difference between Data Science, Machine Learning and Big Data!
Now, let’s delve into what is Machine Learning.
What is Machine Learning?
According to SAS, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”
Even though the term machine learning has been under the spotlight only recently, the concept of machine learning has existed since a long time, the earliest example of it being Alan Turing’s Enigma machine that he developed during World War II. Today, machine learning is almost everywhere around us, right from the ordinary things in our lives to the more complicated calculations involving Big Data. For instance, Google’s self-driving car and the personalized recommendations on sites such as Netflix, Amazon, and Spotify, are all outcomes of Machine Learning.
How Do Machines Learn?
To better understand the question “what is Machine Learning,” we have to know the techniques by which machines can ‘learn’ by themselves. There are three primary ways in which devices can learn to do things – supervised learning, unsupervised learning, and reinforcement learning. While nearly 70% of ML is supervised, only about 10-20% of ML is unsupervised learning.
Supervised learning deals in clearly defined and outlined inputs and outputs and the algorithms here are trained through labeled tags. In supervised learning, the learning algorithm receives both the defined set of inputs along with the correct set of outputs. So, the algorithm would then modify the structure according to the pattern it perceives in the inputs and outputs received. This is a pattern recognition model of learning that involves methods such as classification, regression, prediction, and gradient boosting.
Supervised learning is usually applied in cases involving historical data. For instance, using the historical data of credit card transactions, supervised learning can predict the future possibilities of faulty or fraudulent card transactions.
Contrary to supervised learning that uses historical data sets, unsupervised learning is apps that lack any historical data whatsoever. In this method, the learning algorithm goes beyond data to come up with the apt structure – although the data is devoid of tags, the algorithm splits the data into smaller chunks according to their respective characteristics, most commonly with the aid of a decision tree. Unsupervised learning is ideal for transactional data applications, such as identifying customer segments and clusters with specific attributes.
Unsupervised learning algorithms are mostly used in creating personalized content for individual user groups. Online recommendations on shopping platforms and identification of data outliers are two great examples of unsupervised learning.
Reinforcement learning is quite similar to traditional data analysis method where the algorithms learn through trial and error method, after which it declares the outcomes with the best possible results. Reinforcement learning is comprised of three fundamental components – agent, environment, and actions. The agent here refers to the learner/decision-maker; the environment consists of all that which the agent interacts with, and the actions refer to the things that the agent can perform.
This type of learning helps improve the algorithm over time because it continues to adjust the algorithm as and when it detects errors in it. Google Maps routes are one of the most excellent examples of reinforcement learning.
Now that you’re aware of what is Machine Learning, including the types in which you can make the machines learn, let’s now look at the various applications of Machine Learning in the world today.