Artificial Intelligent

Machine Learning

Machine learning is a data analysis method that learns itself by analytical models. It is the main tool of artificial intelligence. The idea is that systems can learn from data, recognize patterns, and make decisions with minimal human intervention.

Machine learning evolution

Due to new computer technology, today’s machine learning is not like the machine learning of the past. It grew out of pattern recognition and the theory that computers could perform certain tasks without programming; Researchers interested in artificial intelligence want to see if computers can learn from data. You learn from previous calculations to get reliable and repeatable results and solutions.

Google self-advertising car? The essence of machine learning.

Do you recommend online products like Amazon and Netflix? Machine learning applications in everyday life.

Do you know what your customers are saying about you on Twitter? Machine learning is combined with the creation of language rules.

Fraud detection? Today one of the most obvious and important uses in the world.

Why is machine learning important?

Data mining and analysis are quite popular these days. Things like an ever-increasing amount and variety of data, cheaper and more powerful computational processing, and affordable data storage.

All this means that models can be generated quickly and automatically, which, even in large situations, can analyze larger and more complex data and provide faster and more accurate results. By creating accurate models, companies are more likely to identify opportunities for profit or avoid unknown risks.

Who uses it?

Most industries that process large amounts of data recognize the value of machine learning technology. By gathering information from this data, usually, in real-time, companies can work more efficiently or gain a competitive advantage.

Financial services

government

Health

retail trade

oil and gas

transport

Which machine learning methods are popular?

The two most widely used machine learning methods are supervised learning and unsupervised learning, but there are other machine learning methods as well.

There are 4 types of machine learning.

Supervised learning

Partially managed training

Intensive learning

Unsupervised learning

IBM has a long history of machine learning. One of its own members, Arthur Samuel, is credited with introducing the term “machine learning” for his research (PDF, 481 KB) on chess (link outside IBM). Robert Neely, who calls himself a master of billiards, played the game on an IBM 7094 computer in 1962 and lost the computer. Compared to what is possible today, this achievement may seem trivial, but it is considered an important step in the field of artificial intelligence.

Machine learning is an important part of the ever-evolving field of data science. Through the use of statistical methods, classification or forecasting training algorithms reveal important information in data mining projects. These insights then stimulate business and application decisions and ideally influence key growth indicators. As big data continues to evolve and grow, the market demand for data scientists will increase, so they must help identify the most relevant business questions and then use the data to solve those problems.

This is how machine learning works

The University of California, Berkeley (link is out of IBM) divides the machine learning algorithm system into three main parts.

Decision-making process: Generally, machine learning algorithms are used for prediction or ranking. Based on some input data, which may or may not be flagged, your algorithm generates an approximate data model.

Error function: The error function is used to estimate the model’s approximation. Once the sample is known, the error function can be compared to assess the accuracy of the model.

Model optimization process: As the model can better fit the data points in the training set, adjust the weights to reduce the difference between known and estimated examples. The algorithm repeats this evaluation and optimization process and automatically updates the weights until the accuracy threshold is reached.

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