Machine Learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Machine learning uses algorithms to build analytical models, helping computers “Learn” from date. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

Understand Types of Machine Learning Algorithms

The application of Big Data is undoubtedly an important part of technological development in the future. However, machine learning and artificial intelligence both play an important role of unleashing the value of data. Machine Learning means machines (computers) have the same learning capability how humans learn. Through the data, machine learning nowadays has been widely used in life.

A. Supervised Learning

All materials are “labeled” to tell the machine the corresponding value to make it predict the correct value. This method is mostly manual classification, which is the easiest for a computer and the hardest for humans. This method is like telling the machine (computer) standard answer. When the machine is officially tested, the machine will reply according to the standard answer, and the reliability will be greater.
For example, if you would like to train a machine to distinguish between elephants and giraffes, you can provide 100 photos of elephants and giraffes. The machine detects the characteristics of elephants and giraffes according to the “labeled” photographs and identifies elephants and giraffes according to their characteristics. In the end, it will correctly predict them.

B. Un-Supervised Learning

No material is labeled, and the machine classifies materials itself by detecting the characteristics of the data. Manually classification is not involved in this method, which is the simplest for humans, but it is the hardest for the computer and caused more errors.
If un-supervised learning is used to identify elephants and giraffes, the machine must decide which of the 100 photos provided are elephants and which are giraffes and do the classification at the same time. In future predictions, the machine identifies which animal it is according to the characteristics and classification it detects. However, the results identified by the machine are not necessarily correct.

C. Semi-Supervised Learning

A small amount of data are labeled. Computers only need to find features through labeled data and then classify other data accordingly. This method can make predictions more accurate and is the most commonly used method. If there are 100 photos, 10 of them which are elephants and which are giraffes are labeled. Through the characteristics of these 10 photos, the machine identifies and classifies the remaining photos. Because there is already a basis for identification, the predicted results are usually more accurate than unsupervised learning.

D. Reinforcement Learning

The machine uses observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Using reinforcement learning, there is no labeled materials, but tell it which step is correct and that step is wrong. According to the quality of the feedback, the machine gradually amends its classification and finally gets the correct result. In order to achieve a certain level of correctness in un-supervised learning, integration of reinforcement learning is necessary. If the machine identifies the features and classifications on its own and predicts the image of an elephant as a giraffe, the human gives the wrong message. The machine will recognize the features and classification again. Through correct and wrong learning at one time, the final prediction will become more and more accurate.

Knowledge Base

How it Works

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information. That includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more.

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information. That includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more.

Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.