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.
Now, let’s delve into the 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 labelled 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 involving 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.
Why Is Machine Learning Important In Today’s World?
After what is machine learning, comes the next important question – “what is the importance of machine learning?”
The main focus of machine learning is to help organizations enhance their overall functioning, productivity, and decision-making process by delving into the vast amounts of data reserves. As machines begin to learn through algorithms, it will help businesses to unravel such patterns within the data that can help them make better decisions without the need for human intervention. Apart from this upfront benefit, machine learning has the following advantages:
Timely Analysis And Assessment
By sifting through massive amounts of data such as customer feedback and interaction, ML algorithms can help you conduct timely analysis and assessment of your organizational strategies. When you create a business model by browsing through multiple sources of data, you get a chance to see the relevant variables. In this way, machine learning can help you to understand the customer behaviour, thereby allowing you to streamline your customer acquisition and digital marketing strategies accordingly.
Real-time Predictions Made Possible Through Fast Processing
One of the most impressive features of ML algorithms is that they are super fast, as a result of which data processing from multiple sources takes place rapidly. This, in turn, helps in making real-time predictions that can be very beneficial for businesses. For instance,
- Churn analysis – It involves identifying those customer segments that are likely to leave your brand.
- Customer leads and conversion – ML algorithms provide insights into the buying and spending patterns of various customer segments, thereby allowing businesses to devise strategies that can minimize losses and fortify profits.
- Customer retention – ML algorithms can help identify the backlogs in your customer acquisition policies and marketing campaigns. With such insights, you can adjust your business strategies and improve the overall customer experience to retain your customer base.
Machine learning has already started to transform industries with its ability to provide valuable insights in real-time. Finance and insurance companies are leveraging ML technologies to identify meaningful patterns within large data sets, to prevent fraud, and to provide customized financial plans for various customer segments. In healthcare, wearables and fitness sensors powered by ML technology are allowing individuals to take charge of their health, consequently minimizing the pressure on health professionals. Machine learning is also being used by the oil and gas industry to find out new energy sources, analyzing the minerals in the ground, predict system failures, and so on.
Of course, all of this is just tip of the iceberg. If you are curious to understand what is Machine Learning in depth, it’s better to look deeper into the technology. We hope we were able to help you understand what is machine learning, at least on the surface. There’s always so much more to do and learn, that merely asking “what is machine learning” will only help a little. It’s your time to dig deeper and get hands-on with the technology!
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