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How Does Machine Learning Work?


How Does Machine Learning Work?


As the quantum of data increases by the day, Machine Learning is the way to go for many companies. Not only does it make data processing easy, but it also cuts your workload in half, which we are sure you would appreciate. With Machine Learning, your computer “thinks almost as humans do” by processing and improving previous knowledge. It does this by evaluating data and recognizing patterns with little to no human involvement. Many different industries can benefit from Machine Learning, including healthcare, financial, retail, oil and gas, transport, etc. With that being said, we’ll now explore what exactly machine learning is and how it works.

What is machine learning?

The big question is, precisely, what is machine learning? Machine Learning in computer algorithms is the use of artificial intelligence (AI). It provides the capacity to learn and develop automatically from experience or historical data without any programming.

Machine Learning goes through different steps in achieving its desired result. It assists with Business Intelligence and data gathering, data preparation, choosing a model, training, evaluation, and prediction. It can further improve computer programs that access and utilize data and enhance their accuracy over time.

Machine Learning can also assist with image processing and recognition, object and motion detection, speech recognition (speech to text, voice searches), medical diagnosis (patient monitoring, diseases detection and diagnosis, DNA sequencing, and drug discovery).


How does machine learning work?

No doubt, Machine Learning can be very instrumental in accelerating work productivity. It works in many different ways and offers various methods to consume and process data—namely supervised learning, unsupervised learning, self-supervised learning, and reinforcement learning. The difference between these methods lies in how the system learns. You could opt for any of these depending on the output you expect and the input you supply. This means that the method you choose depends on the kind of data you provide and the outcome you seek.

Let’s now explore the various individual methods of Machine Learning.

1. Supervised Learning

With supervised learning, results are already known from which the system can learn. This machine uses known data in processing and execution. According to the trained solution space, it pretty much learns to use the given data to classify and predict inputs. This method can help to generate predictions based on concrete facts, even in the face of uncertainty. You can also use supervised learning when you have known data for the desired outcome. As learning progresses, predictions or categorizations become more precise.

2. Unsupervised Learning

With unsupervised learning, the input training data isn’t designated, and the results are unknown. It uses the inputs to independently build a model that tries to predict the inputs as precisely as possible. The system is not spoonfed but has to compute or puzzle out what it’s given by either categorizing similar data or identifying concealed patterns. Clustering (which is the most popular unsupervised learning technique) is used in descriptive data analysis to locate hidden structure data.

3. Self-Supervised Or Semi-Supervised Learning

Semi-supervised learning lies somewhere between supervised and unsupervised machine learning. Systems that adopt this method can substantially enhance learning accuracy. Semi-supervised learning is used when data requires efficient and suitable resources to train/learn from. It applies labeled and unlabeled data for learning.

4. Reinforcement Learning

This learning method interacts with its surroundings by generating actions while discovering inaccuracies or rewards. It allows machines to automatically establish the right behavior at a particular time to improve their performance. The machine, through trial and error, discovers which actions give the right responses. When reinforcement learning performs a task efficiently, it gets positive feedback, reinforcing the model in connecting target inputs and output. This is often used in gaming, data management, navigation, and robotics.

Calvin M. Barker

Typical tv scholar. Problem solver. Writer. Extreme bacon fan. Twitter maven. Music evangelist. Spent a year consulting about salsa in Fort Lauderdale, FL. Spoke at an international conference about lecturing about junk food in New York, NY. Earned praise for promoting robotic shrimp in Phoenix, AZ. Spent 2002-2007 working on catfish in Naples, FL. Spent several months developing yogurt in Orlando, FL. Spent high school summers managing dandruff in Africa.