Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn, without being explicitly programmed.
Machine learning focuses on developing computer programs that can change when exposed to new data.
The machine learning process is similar to that of data mining. Both systems search through the data to find patterns.
However, instead of extracting the data for human understanding - as is the case in data mining applications - machine learning uses that data to detect patterns in the data and adjust program actions accordingly .
Machine learning algorithms are often classified as supervised or unsupervised. Monitored algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from data sets.
The Facebook news feed uses machine learning to personalize each member's feed . If a member frequently pauses to read or "like" a particular friend's posts, the news feed will start showing more activity from that friend earlier in the feed .
Behind the scenes, the software simply uses statistical analysis and predictive analytics to identify patterns in user data, and use patterns to populate the news feed .
In case the member no longer pauses to read, like or comment on the friend's messages, that new data will be included in the dataset and thenews feed will be adjusted accordingly.
Tom Mitchell defines machine learning in one of his books as: “The study of computing algorithms that automatically improve their performance thanks to experience.
It is said that a computer program learns about a set of tasks, thanks to experience and using a performance measure, if its performance in these tasks improves with experience. "
In other words, algorithms that learn and improve "on their own" thanks to experience. This fact that they do it alone is in quotes because they do it using data, past experiences.
Unlike models in which a business expert assigns rules and models something based on their knowledge (their past experience), statistical models and machine learning models let the data do the talking and get the relationships automatically.
Evolution of machine learning
Due to new computing technologies, machine learning today is not like it was in the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks;
Researchers interested in artificial intelligence wanted to know if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they can be adapted independently.
They learn from prior calculations to produce reliable and repeatable decisions and results. It is a science that is not new - but one that has gained new momentum.
Although many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data - again and again, faster and faster - is a recent achievement.
Here are some widely published examples of machine learning applications that you may be familiar with:
Google's highly hyped self-driving car? The essence of machine learning
Online referral offers like Amazon and Netflix? Machine learning applications for daily life.
Know what customers are saying about you on Twitter? Machine learning combined with the creation of linguistic rules.
Fraud detection? One of the most obvious and important uses in our world today.
Why is machine learning important?
The resurgence of interest in machine-based learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever.
Things like the increasing volumes and varieties of data available, cheaper and more powerful computational processing, and affordable data storage.
All of these things mean that it is possible to quickly and automatically produce models that can analyze larger and more complex data and produce faster and more accurate results - even on a very large scale.
And by building accurate models, an organization has a better chance of identifying profitable opportunities - or avoiding unknown risks.
Who uses it?
Most industries that deal with large amounts of data have recognized the value of machine learning technology.
By gaining insights from this data - often in real time - organizations can work more efficiently or gain an advantage over their competitors.
Financial services
Banks and other companies in the financial industry use machine learning technology for two main purposes: to identify important insights in the data and to prevent fraud.
Insights can identify investment opportunities or help investors know when to buy or sell. Data mining can also identify customers with high-risk profiles or use cyber surveillance to detect warning signs of fraud.
Government
Government agencies such as public safety and utilities have a particular need for machine learning because they have multiple data sources from which insights can be drawn.
For example, analyzing sensor data identifies ways to increase efficiency and save money. Additionally, machine-based learning can help detect fraud and minimize identity theft.
Health care
Machine learning is a rapidly growing trend in the healthcare industry, thanks to the emergence of wearable devices and sensors that can use data to assess a patient's health in real time.
Likewise, technology can help medical experts analyze data to identify trends or red flags that can lead to improved diagnoses and treatments.
Marketing and sales
Websites that recommend items you might like based on past purchases use machine learning to analyze your purchase history - and promote other items that might interest you.
This ability to capture data, analyze it, and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail.
Oil and gas
How to find new sources of energy. Soil mineral analysis. Prediction of refinery sensor failures. Optimization of oil distribution to make it more efficient and economical.
The number of machine learning use cases in this industry is vast - and it continues to grow.
Transport
Analyzing data to identify patterns and trends is key for the transportation industry, which relies on making routes more efficient and anticipating potential problems to increase profitability.
The data modeling and analysis aspects of machine learning are important tools for courier companies, public transportation, and other transportation organizations.
But why is there so much talk today about machine learning?
Many of the methods used in machine learning and statistical modeling have been with us for several decades. Algorithms such as neural networks or vector support machines (SVMs) were devised a long time ago, some of them even fell into disuse.
One of the main reasons for the current boom in these techniques are:
On the one hand, the computational capacity of computers has been increasing and it is currently possible to treat problems that could not be treated before.
This increase has been vertical (improvement in individual computing capacity, CUDAs ...) and also horizontal (increase in computing capacity when working with several computers at the same time using the Big Data paradigm).
On the other hand , the data revolution , motivated by digitization, has led to a huge increase in data that can be processed and modeled to gain knowledge of them. Years ago there was much less data, being possible to see statistical models of a few hundred records.
Today we live in an exciting period in which data and the application of techniques that extract value from it will be strategic for many countries and sectors.
You just have to see the investment that China, the US and other countries are carrying out to realize that Machine Learning and current modeling techniques are present and future.
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