Machine Learning is one of the most trending technologies available today. In this blog, you will learn about some of the most popular and widely used Machine Algorithms. However, let’s first try to understand the meaning of Machine Learning and its algorithms.

**What is Machine Learning?**

Machine Learning (ML) allows systems to gain knowledge from past information and experiences to improve their performance without being explicitly programmed. It uses Deep Learning and other advanced technologies in order to help the systems learn.

To learn in-depth about Machine Learning and become proficient, sign up for an online Machine Learning Course.

You will now read more about Machine Learning algorithms and its various types.

**What do you mean by Machine Learning Algorithms?**

Machine Learning algorithms are algorithms that assist in making your programs better and smarter by helping them to learn from the given data automatically. These ML algorithms can be broadly classified into the training phase and the testing phase.

**Training Phase**

During the Testing Phase, you can randomly choose the training data from the huge pile of available data sets. Further, you can create a table containing all the features and characteristics based on the training data set. Your table can also include the output variables. Once you complete making the table, you can feed this information to the Machine Learning algorithms. The algorithm then learns the correlation model of the various features and variables mentioned in the table.

**Testing Phase**

When you feed new data to the algorithm, it will make use of the earlier model in order to predict the necessary information based on the data sets. The ML algorithm may follow rules that are similar to the one you entered before. Finally, the machines can make predictions of the future data based on the past data fed to them earlier.

**4 Types of ML Algorithms**

There are mainly three types of Machine Learning algorithms – supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning algorithms.

**Supervised ML Algorithms**

Supervised learning algorithms are those that include direct supervision of the given business operation. They contain outcome variables/targets/dependent variables that are to be predicted from a set of independent variables provided. With the help of these variables, you can build a function that allows you to map inputs to the respective outputs. The process of training continues until the Machine Learning model gets the desired accuracy level based on the training data set.

**Unsupervised ML Algorithms**

Unsupervised learning does not consist of the direct control of the programmer. Unlike supervised learning, in this algorithm, the data fed to the machines is unlabelled and the results are undefined. It does not have a target for estimation. It is generally used to group the data into clusters and segment the users in various groups for intervention.

**Semi-supervised ML Algorithms**

This type of Machine Learning algorithm is a combination of some features of both supervised and unsupervised learning algorithms. It makes use of a limited labeled data set sample in order to meet the operation requirements resulting in a partially trained Machine Learning model. Further, this model works on labeling the data that were unlabelled. This data is considered pseudo labeled data. In the end, this data is combined with the labeled data set, developing a discrete algorithm that involves both predictive and descriptive features.

**Reinforcement ML Algorithms**

Reinforcement learning is used to build self-sustained machines that have the capability of improving themselves based on the labeled data combined with the incoming data. The machine learns continuously using hit and trial methods in order to gain knowledge and make accurate decisions.

Now, get an overview of some of the most popular ML algorithms.

**Top 5 ML Algorithms**

Here are the five most commonly used Machine Learning algorithms.

**Linear Regression**

This algorithm helps in estimating the real values on the basis of continuous variables. It establishes a relationship between dependent and independent variables by making them fit into a single line called a regression line. It is denoted by Y = a*X + b linear equation where:

- Y: Dependent variable
- a: Slope
- X: Independent variable
- b: Intercept

In this, a and b are coefficients derived on the basis of minimizing the total of the difference of the squared distance in between the regression line and the data points.

**Logistic Regression**

Logistic Regression allows you to predict discrete values (generally binary values such as 0 or 1) from the given set consisting of independent variables. It interprets the event probability by inserting the data into a logit function. This is why this regression method is also referred to as logit regression.

Following are some of the methods used to make improvements in logistic regression models:

- Use non-linear models
- Regularize techniques
- Eliminate irrelevant features
- Include interaction terms

**Decision Tree**

Decision Tree is one of the most commonly used ML algorithms. It is a supervised learning algorithm that generally helps in solving classification issues. It works extremely well with both continuous and categorical dependent variables. Here, you can split the variables set into at least two homogenous sets on the basis of independent variables.

**Support Vector Machine (SVM)**

Support Vector Machine is a classification method where you can plot all the raw data items in an n-dimensional space in which n represents the number of features. Each feature value is then associated with a specific coordinate in order to ease the classification of data. You can use lines or classifiers to split that data in order to plot them.

**Naïve Bayes**

This classification technique is based on the assumption that the features in a class are unrelated to the presence of each other. Although these features might be related, the Naïve Bayes classifier considers them independent while calculating the probability of any specific outcome.

This model can be built easily and is mainly useful for huge sets of data. It is simple and also has the ability to outperform highly sophisticated methods of classification.