Machine Learning Techniques | Top 4 Techniques of Machine Learning

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Machine Learning Techniques (like Regression, Classification, Clustering, Anomaly detection, etc.) are used to build the training data or a mathematical model using certain algorithms based upon the computations statistic to make prediction without the need of programming, as these techniques are influential in making the system futuristic, models and promotes automation of things with reduced cost and manpower.

There are a few methods that are influential in promoting the systems to automatically learn and improve as per the experience. But they fall under various categories or types like Supervised Learning, Unsupervised Learning, Reinforcement Learning, Representation Learning, etc. Below are the techniques which fall under Machine Learning:

Regression algorithms are mostly used to make predictions on numbers i.e when the output is a real or continuous value. As it falls under Supervised Learning, it works with trained data to predict new test data. For example, age can be a continuous value as it increases with time. There are some Regression models as shown below:

Some widely used algorithms in Regression techniques

A classification model, a method of Supervised Learning, draws a conclusion from observed values as one or more outcomes in a categorical form. For example, email has filters like inbox, drafts, spam, etc. There is a number of algorithms in the Classification model like Logistic Regression, Decision Tree, Random Forest, Multilayer Perception, etc. In this model, we classify our data specifically and assign labels accordingly to those classes. Classifiers are of two types:

Clustering is a Machine Learning technique that involves classifying data points into specific groups. If we have some objects or data points, then we can apply the clustering algorithm(s) to analyze and group them as per their properties and features. This method of unsupervised technique is used because of its statistical techniques. Cluster algorithms make predictions based on training data and create clusters on the basis of similarity or unfamiliarity.

Anomaly detection is the process of detecting unexpected items or events in a data set. Some areas where this technique is used are fraud detection, fault detection, system health monitoring, etc. Anomaly detection can be broadly categorized as:

There are certain techniques in Anomaly detection as follows:

Machine Learning utilizes a lot of algorithms to handle and work with large and complex datasets to make predictions as per need.

For example, we search a bus image on Google. So, Google basically gets a number of examples or datasets labeled as bus and the system finds the patterns of pixels and colors that will help in finding correct images of the bus.

Google’s system will make a random guess of the bus like images with the help of patterns. If any mistake occurs, then it adjusts itself for accuracy. In the end, those patterns will be learned by a large computer system modeled like a human brain or Deep Neural Network to identify the accurate results from the images. This is how ML techniques work to get the best result always.

Machine Learning has various applications in real life to help business houses, individuals, etc. to attain certain results as per need. To get the best results, certain techniques are important which have been discussed above. These techniques are modern, futuristic and promote automation of things with less manpower and cost.

This has been a guide to Machine Learning Techniques. Here we discuss the basic concept with some widely used techniques of machine learning along with its working. You may also have a look at the following articles to learn more–