Have you encountered challenges in understanding machine learning, its importance, types, and application in the real world? This article promises to aid your understanding of machine learning and its significance in the world today.
What is Machine Learning?
According to IBM, “Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”
In simpler form, machine learning is an application of AI that equips systems to learn automatically from examples or experiences without being explicitly coded by a programmer.
According to Nvidia, “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”
Machine learning doesn’t just stop at equipping systems alone. It also aids them in building models through which decisions and predictions can be made on data points it has not seen before.
Analytics Insight describes it like this, “Machine learning, as the name suggests, is a process in which machines learn and analyze the data fed to it and predict the outcome.”
Why is machine learning important?
“The goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses.”
- It identifies potentially profitable opportunities for your business
Machine learning processes big and complex data. It doesn’t just stop there. Rather, it goes further to give predictions about the future.
With the information provided, machine learning presents your business with profitable opportunities it can take advantage of for increased growth.
2. It guides the decision-making process of your business.
By its ability to analyze data and predict future outcomes, machine learning helps to guide the decision-making process of your business.
“Using data in decision making will keep your business heading in the right direction, optimizing your current and future operational efforts and making it easier to produce insights that can be quickly acted upon in the best interests of the company.”
Decision-making is easier with machine learning.
3. It reveals potential risks.
“Assessing and prioritizing risks is extremely important to ensure the adequate focus of the management on critical areas.”
By utilizing machine learning, your company is able to assess and properly handle risks. This in turn allows you to focus on other critical areas of your company.
4. Automated processes
ML allows companies to transform processes that have only been possible for human beings to perform in times past.
In the words of Dave Waters, “Machine learning will automate jobs that most people thought could only be done by people.”
5. Improved customer experience
“57% of enterprise executives believe that the most important growth benefit of AI and machine learning will be improving customer experiences and support.
Customer service remains a pivotal part of every business no matter the size or industry. ML seeks to transform the customer service experience completely in the coming years.
In a nutshell, machine learning is the best way to build sustainable models, strategize and plan for the future of your business.
Types of Machine Learning
There are three types of machine learning: Supervised, unsupervised, and reinforcement learning.
1. Supervised Learning
In a supervised learning model, the algorithm learns on a labeled dataset and generates predictions based on past experience in order to respond to new data.
Say, for example, we are trying to predict flight tickets for new airways. Firstly, we would need to gather data about flights such as its features, what it offers and so on. Then, we need to find out the prices of those flights.
Now, with the data from various airways, their features, and prices, we can train a supervised ML model to predict the price of flight tickets for new airways based on the past experiences of that model.
Supervised Learning consists of two types: classification and regression.
2. Unsupervised Learning
Unlike the supervised learning model, the algorithm learns on an unlabeled dataset. In this case, the algorithm will be fed with data and the necessary tools to understand its properties.
From there, it can learn to group or organize the data in a manner that humans or intelligent algorithms can easily make sense of any newly organized data.
Unsupervised learning is so interesting because a vast majority of data in today’s world is unlabeled. Therefore, unsupervised learning takes unlabeled data and makes sense of it.
As a result, this sets your company up for a potential increase in profit margins and also a boost in productivity.
Unsupervised learning has various types. Such as clustering, anomaly detection, association, autoencoders, latent variable models, and Neural Networks.
3. Reinforcement Learning
When it comes to reinforcement learning, the model learns how to behave in a certain environment by taking actions and analyzing the reactions.
This model is bound to learn from the experience itself by making use of a trial and error method. So, it encourages or reinforces positive outputs and discourages negative ones.
For example, when a baby is learning how to walk. If the baby is able to walk into the open arms of mum/ dad who gives them a twirl, then the baby is happy. That’s a positive response. Because, that was the end goal for the baby- to meet mum/dad.
If in another instance, while learning how to walk she hits a chair and couldn’t reach Mum/dad, the baby falls down and starts crying. This is a negative response.
How does this relate to reinforcement learning? The baby tries different methods to get to Mum/dad (the reward), analyzes the reactions from those methods, and obtains the best way to reach that reward.
Likewise, the model derives the best course of actions from previous actions taken in that environment and analyzed reactions.
Few Real-World Applications of Machine Learning
- Automatic Language Translation in Google Translate
- Smartphone with face recognition
- Driverless/Self-driving car
- Netflix Recommendation System
- Weather Prediction
Use cases of ML in various industries
According to Industry Research, the Global Machine Learning market is projected to grow by USD11.16 billion between 2020 and 2024, progressing at a Compound Annual Growth Rate (CAGR) of 39% during the forecast period.
This data reveals that ML is growing across the world. Now, let’s talk about how various industries are using ML.
- Credit default rate
Using ML, financial institutions can build models to assess risk, score credit card needs, and also make more accurate predictions.
E-commerce is experiencing rapid increase and growth as many people shop online nowadays. Hence, they need help to effectively serve their customer base. ML plays a pivotal role here by helping them track their customer needs, monitor trends, predict the behavior of their customers, and so on.
For example, have you ever bought items from any online shopping platforms only to start seeing recommendations of similar items on your timeline? That’s ML at work.
Have you ever had problems with the delivery services of any business? It can be very frustrating.
One key factor for operating any form of delivery/ logistics service is efficiency. For ease in transportation, ML makes it possible to optimize carrier selection, rating, routes, and other processes. This helps to save money and improve efficiency.
4. Fraud detection
ML models can dramatically improve the customer verification process that predicts fraud in real-time. Lenders can use ML models to train historical application data to uncover relationships between application variables and the likelihood of fraud.
Other industries utilizing ML include cyber security, manufacturing, media, and many others.