Machine learning is the new big thing in data analysis. Companies of all kinds are investing heavily in machine learning as a technique for analyzing data and making sense of it. It can analyze vast amounts of structured and unstructured data to arrive at the best insights possible.
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Thus, to stay competitive, you need to understand what types of machine learning algorithms are out there and how they will help you make better decisions. Here’s a brief overview of the main types of machine learning algorithms available today so that you can get up to speed quickly.
Supervised machine Learning algorithms
Machine learning algorithms rely on data to create models that generate predictions. While they can make accurate predictions, they only work if they are trained on examples. Some of the most important supervised machine learning algorithms are as follows.
- supervised learning – This is when an algorithm has a set of known data and a set of known answers. For example, a computer model might be trained to recognize the difference between pictures of dogs and pictures of cats so that it can correctly identify pictures of cats when someone asks it to do so.
- Unsupervised learning – This type of learning doesn’t necessarily require a set of known answers but rather a set of known data. An algorithm might be trained to recognize patterns in large amounts of unstructured data like images, audio, or text. For example, an algorithm might be trained to recognize the difference between images of cars and images of dogs, so that it can correctly identify images of cars when someone asks it to do so.
Reinforcement Learning is a type of Machine Learning where a computer learns by trial and error. A human being can learn by trial and error too but it is much more efficient if the person has an intuition about what is “right” and “wrong.” In reinforcement learning, the computer doesn’t know what is “right” or “wrong” but it does have an intuition about what is likely to happen next based on the past. Humans are good at this intuitive type of learning because we have an internal model that tells us if we are going in the right direction or not.
Discrete Event Simulation (DESS)
There are many ways to simulate the actions of an agent that interact with their environment. However, most simulation algorithms are designed to work with continuous data. DESS allows you to create simulations of discrete events, like a computer making a decision, interacting with its environment, and then experiencing the consequences of that decision.
Finite Element Method (FEM)
FEM is a numerical method that allows you to solve engineering problems involving solids, liquids, and gases by modelling them as collections of elementary parts, like a bucket filled with water. Machine Learning algorithms that use FEM are useful when you need to forecast the outcomes of complicated systems, like the weather. Predictive algorithms that use this method are often used in the insurance sector to predict the probability of a natural disaster happening.
Object-oriented approach with code generation
In a world where data is exploding, organizations are looking for any type of edge they can get over their competition. To do this, they are turning to object-oriented programming (OOP) to build their data architectures. They are also turning to code generation to develop their own machine learning algorithms.
Some key takeaways
Machine learning is a hot new technology that is helping organizations gain a competitive edge by analyzing data and making decisions. However, because of the vast amount of information out there, it can be difficult to sift through and find the right resources for your specific needs. This is why it’s important to understand the major types of machine learning algorithms available and how they can help you make better decisions.