What is Machine Learning?

The Machine Learning concept, a branch within Artificial Intelligence that provides computers with automatic learning without the need to be continuously programmed, has taken on greater prominence in the last decade. In just a few years, algorithms classified as Machine Learning have evolved to manage large volumes of data (Big Data), obtain better results, and solve problems more efficiently.

Its use is increasingly varied. In fact, according to estimates by the consulting firm Accenture, its application will increase the productivity of companies by more than 40% by 2035. In addition, currently, more than 60% of CEOs are already using AI in their automation processes. and 25% of companies invest up to 44 million euros in modifying and reorienting their business models towards algorithms, according to the latest KPNG report.

Although it may seem that we are talking about software development solutions of the future, the reality is that its practical application is part of the daily lives of the world's population.

The best machine-learning applications

Financial sector

In the financial sector, custom development services such as Machine Learning is key for credit evaluation since large sets of data can be analyzed to evaluate the solvency of applicants. These models can take into account a variety of factors, including credit histories, income, expenses, and financial behaviors, to predict the likelihood that an individual or business will default on their payments.

Machine Learning algorithms are effective at identifying patterns and anomalies in financial transactions that could indicate fraudulent activity. These models can analyze large volumes of data in real-time and improve their accuracy as they are exposed to more information.

These models also help evaluate and manage risks and can analyze factors such as interest rates, market fluctuations, geopolitical events, and other indicators to predict possible risks and assist in decision-making.

In addition, they personalize products and services and are capable of automating processes. Another function they can perform is managing portfolios and algorithmic trading.

Marketing

Machine Learning models can segment the audience by analyzing data and purchasing behaviors. Machine learning algorithms can analyze user behavior to predict what type of content a particular individual is most likely to be interested in.

Another use that can be given is to automatically recommend products, optimize prices and automate advertising.

Virtual assistance

Regarding virtual assistance, Machine Learning can process natural language learning automatically based on a series of labeled data or with unlabeled patterns and relationships. In addition, it can recognize voice and personalize the interaction by generating text.

Health

The application of Machine Learning in the healthcare field has been revolutionary, offering significant advances in diagnosis, treatment, data management, and patient care. In surgery, ML-assisted robotics can improve precision and efficiency. 

Communications

Machine Learning has significantly influenced the communications sector, improving efficiency, personalization, and security.

Thanks to ml, automatic translations are carried out and algorithms can recognize voices. Another application is spam filtering and social media optimization.

Security

The application of Machine Learning in cybersecurity has been increasingly crucial in addressing ever-evolving digital threats.

ML algorithms can learn the normal behavior of systems and networks to detect anomalies that could indicate malicious activity. This is especially useful for identifying unknown threats or sophisticated attacks.

They can also analyze code and behavior patterns to detect malware. It is used to improve biometric authentication.

Examples of use of Machine Learning

1. Face detection. Facial recognition is one of the most important revolutions of the decade. It is used to unlock your phone, test Snapchat or Instagram filters, and even try to predict how you age. Although it seems somewhat new, the first time it was used was at the end of the 19th century by French police officer Alphonse Bertillon with the aim of identifying the faces of criminals and replacing the fingerprint method. The software identifies faces using a group of 68 specific references or points, more or less, whose configuration is different for each person.

2. Speech recognition. The first voice recognition systems were created in 1952 and were based on the speaker's voice power. Currently, there are systems such as: “Ok Google” or “Hey Siri”, among others. This is one of the best examples of Machine Learning. In order to better understand what is needed when asking a question, these virtual assistants end up knowing everything about the user such as: sleep patterns, messages, calendar, reminders, emails ...

3. Gmail. By marking emails as malware, the system ends up understanding and learning to send these messages directly to the “junk” folder to keep the user protected from viruses, fraud, or messages that do not interest them.

4. Personalized marketing. Based on the user's actions when they use the Internet, their social networks, or how they interact, ML learns from that behavior to recommend products or services that fit them and thus produces personalized marketing based on behavioral patterns. Companies such as Google, Amazon, and Instagram, among others, work with this data, as it increases the efficiency and productivity of campaigns. In fact, thanks to AI, companies can know the user's needs before they themselves know it.

5. Google Maps for traffic. Every day more than 1 billion km are traveled around the world using Google Maps. This tool shows the safest and most efficient routes using software product development services based on traffic and mobility patterns collected over time and combining it with live traffic conditions. In this way, Machine Learning is applied to generate forecasts based on both data sets.

6. Autonomous cars. Currently, there are cars capable of driving autonomously, overtaking, parking or performing any type of maneuver. These types of cars offer the possibility of reducing traffic incidents and even the number of accidents, since, by eliminating the human factor from the equation, the margin of error is practically non-existent.

7. Medical diagnoses. The use of intelligent systems within medicine has great potential, since they allow a large amount of information to be processed and diagnoses generated, helping to detect pathologies more quickly and with a smaller margin of error than a human being would.

The main areas in which Machine Learning is used are : oncology, which has demonstrated 90% effectiveness in detecting breast and prostate cancer; neurology, where great advances have been made in the diagnosis and treatment of stroke, Alzheimer's or senile dementia; gynecology, helping to detect malformations or problems during pregnancy, and genetics, with programs capable of detecting more than 8,000 genetic disorders and rare diseases using the face.

Author Bio:

Glad you are reading this. I’m Yokesh Shankar, the COO at Sparkout Tech, one of the primary founders of a highly creative space. I’m more associated with digital transformation solutions for global issues. Nurturing in Fintech, Supply chain, AR VR solutions, Real estate, and other sectors vitalizing new-age technology, I see this space as a forum to share and seek information. Writing and reading give me more clarity about what I need.