Explaining Machine Learning in Simple Terms

Today, I would like to talk about machine learning (ML) and put it simply for you. In a nutshell, what exactly machine learning is; how it is applied in practice and what potential it has. I will illustrate the points with simple examples to make it easier to understand.

Many people think that machine learning is something about image recognition, but this perception is completely narrow. Machine learning is a whole world that changes industries for the better and affects lives of us all.

So, what exactly is machine learning?

Machine learning is a data analysis method that identifies patterns and algorithms, learns from them and utilizes for making accurate predictions and better decisions without or with minimal human guidance.

The key word here is data. Machine learning learns (we cannot avoid tautology here) from patterns, predicts probable scenarios and suggests solutions based on the patterns it has already seen and recognized.

What is the main purpose of machine learning?

Machine learning facilitates data-driven decision-making, and this is huge. No matter where applied, such an approach brings only benefits.

To avoid unsubstantiated statements, let’s see how ML is applied in fundamental sectors.

Money, Money, Money: Machine Learning in Finance

In the article about financial software I mentioned how machine learning can be applied for risk identification in situations related to finance where decisions must be made instantly, аnd if made by people, they would take too much time.

Let us look at a simple example that illustrates the enormous value machine learning may bring to both financial institutions and their clients.

Bank credits: who decides whether you are a trustworthy person? If you do not have a credit story or cannot prove your ability to pay it back, bankers will most likely decide to deny your application, even if they are wrong. This is unfair, don’t you think so?

On the other hand, put yourself in the shoes of banks: how can they decide, to give a loan to a person or to refuse them? There is a lack of data for such a decision, and the probability of error is too high.

That’s where machine learning comes in. It monitors various data on creditworthiness, assesses risks and predicts a probability a person will repay the loan.

In effect, it is a win-win situation. Banks protect themselves from undesirable scenarios and common people who were previously labeled as untrustworthy are now given an opportunity to clear their name and receive credit.

Machine learning can also be applied for investment decisions. If you are in doubt where to invest, machine learning will consider all options and choose the best ones (don’t put all eggs in one basket) for you.

Just What the Doctor Ordered: Machine Learning in Healthcare

In healthcare accurate predictions can save people’s lives. Machine learning is a good helper in analyzing MRIs, X-rays and other medical images. It assists doctors in diagnosing various diseases, and a proper diagnosis is half the battle. What is more, machine learning allows health professionals to detect anomalies and reduce risks of medication errors.

The right diagnosis and early detection of anomalies or complications allow doctors to take timely measures and enable patients to get the proper treatment and beat the disease.

There is a study where machine learning was considered as a power to identify high-risk patients after surgeries, facilitate doctors to reduce complications and mortality.

So, it is not an exaggeration to say that machine learning has great potential in diagnosing and anomalies detection. It helps doctors to make the right decisions and to generally take healthcare to the next level.

Non-Standard Ways to Apply Machine Learning

We have talked about machine learning within the framework of two major industries: finance and healthcare. However, the feature of machine learning is that it allows people and businesses to move beyond and apply its might in the most unexpected and extraordinary areas. After all, machine learning analyzes data, and data is the foundation for everything.

Combating Fake Reviews

Everyone wants to read only fair and honest reviews, whether it is a restaurant or hotel description on TripAdvisor or Booking.com or comments to an app in the Play Store. But some companies do not afraid to use dishonest means to draw customer attention. They write fake words of praise for themselves from fake accounts and sometimes even post negative feedbacks about their competitors. Thus, both businesses and customers suffer from unfair competition practices.

Machine learning provides a solution even in such cases. A good example is a machine learning powered system developed by Google to fight against paid reviews and fake ratings in the Play Store.

This ML-based system managed to get rid of millions of fake reviews within just a week. In addition, it allowed to track and eliminate a substantial amount of unreliable applications with fake reviews from the Play Store.

If all online review and rating platforms started to utilize machine learning to separate the wheat from the chaff, the quality of products and services would improve markedly and everyone would be better off.

Finding Bugs in Software

Automation testing is already a good support for software developers and QA engineers. It coexists peacefully with manual testing and contributes to qualitative software development.

However, there are no boundaries to perfection, and machine learning could turn software bugs into a thing of the past. Machine learning is able to learn what the bugs are and find vulnerabilities in software solutions thus enabling IT companies to release top-tier products.

That could also solve the information security problem: with no breaches in software, company and customer data will not be compromised and businesses will be able to avoid both reputational and financial losses.

Machine learning has every chance to become an essential part of software development process and a right hand of every developer. Not only will it find bugs and vulnerabilities, but it will also automatically fix them without diverting software engineers from code writing.

Help with Career Choices

Let’s dream a little bit: what if when selecting a college or university you could have got accurate information on careers where you would succeed? How many people wanted to become a pilot, surgeon, ballet dancer or fashion designer but didn’t believe in themselves and in fear of failure ended up being a run-of-the-mill worker not satisfied with their job?

What if machine learning could have predicted your career and your chances of success in a particular profession? Like, for example: it is 63% you will become a respected university professor (definitely worth trying) or 77% you will make a virtuoso musician, while molecular physics is good as a hobby, not a job — only 5% probability of success.

Machine learning is perfectly capable of connecting people with careers they will love. In the end, doing what you love is already beating the odds.

P.S. To even better understand the diverse capabilities of machine learning, take a look at real challenges it solves on Kaggle, a platform for data science competitions. You won’t be disappointed.