**Overview of Machine Learning**

Everyone knows about Artificial Intelligence (AI) – the one broad field in computer science which aims at building an entity that can perceive and reason about the world just like humans. Machine Learning (ML) is the largest sub field of AI and a powerful tool for most data scientists or statisticians, which uses regression to make highly accurate and actionable predictions about products, customers, marketing efforts and applications.

Machine learning uses three methods of learning to predict the outcome of events, which can be broadly classified into:

**Supervised Learning**

In a predicament, supervised learning comes to power; it uses existing data with existing results and uses both input and output data to form predictions for new data. Therefore, previously set examples are used to reach a new goal for a complicated task. The technique of supervised machine learning uses classification and regression to solve problems.

**Unsupervised Learning**

Unsupervised learning involves training the machine to use data which is not well labelled as in; it might have input and no proper labelled output for the same. It allows the algorithm to act on any given data, where it maps and looks for common patterns and intrinsic details to find an answer to the task at hand. It uses a method called clustering to detect and analyse any data that is hidden.

**Reinforced Learning**

It is a branch of AI where the learning involves a trial and error method. It analyses data predict possibilities and generates an answer that helps maximize performances and give accurate results. Reinforced machine learning maintains a reward system which is also a reinforcement signal to promote and boost efficiency.

**How to choose the right ML algorithm?**

Performance isn’t the only thing you are looking for when it comes to finding the correct algorithm. Instead, consider how to use them. For any ML algorithms considering both predictions and run-time are essential, but it’s not everything. Some algorithms are easy to explain, while some are robust to data with outliers and missing values. There isn’t one perfect algorithm for a problem, but several that are good enough to find a fit. Some of the most popular algorithms of ML can be classified as follows:

**Logistic Regression**

This algorithm uses an odd log-ratio that comes into play when the label of the problem is discrete or categorical. It provides optimal solutions such as categorizing the variables and predicting the ones that have response rates which are higher to the target variable.

**Linear Regression**

Linear regression is another method used in problems where the target variable does not have a steady nature. It finds the best fit for the target variable and the variable(s) that explains it very well.

**Random Forest**

This algorithm is mostly suitable for large data sets with outliers or missing values and records the fastest run time. It operates by constructing multiple numbers of decision trees (creates a forest) to predict robust data- as higher the number of decision trees, more accurate the predictions will be.

**Clustering**

The learning algorithm comes under unsupervised algorithms; it is mainly used in problems which require the unveiling of hidden and intrinsic data. It categorises the data points in groups and then predicts the desirable outcomes.

**Support Vector Machines or SVM’s**

Given a data, SVMs use a technique called the kernel trick to change and extract relevant data points completely. It is done within an optimal boundary (not necessarily always a straight line) that can capture complicated relationships between the data points. SVMs are the most popular ML algorithms used to deal with problems such as image segmentation and the stock markets.

*Conclusion*

Before identifying which algorithm is the best for a given problem, the problem needs to be clearly defined and data needs to be gathered accordingly.

The next step involves understanding which of the algorithm family the question belongs to – whether it belongs to two-class classification or multi-class classification, regression or clustering etc.

The final step is to identify one of those algorithms from the family to run the data through and conclude results. It is very crucial to use the proper algorithm based on gathered data and domain to develop an efficient machine learning project.

This article is an updated version of the article titled – **Most Popular Regression Algorithms In Machine Learning**

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