The concept of manual is evolving in a world when almost all manual operations are mechanized. Computers can play chess, conduct surgery, and develop into smarter, more humanlike machines with the aid of machine learning algorithms.

We are at a time of continual technological advancement, and by seeing how computers have developed through time, we may make predictions about what will happen in the future.

The democratization of computer tools and methods is among the revolution’s key distinguishing characteristics. Data scientists have created powerful data-crunching computers during the last five years by effortlessly implementing cutting-edge methodologies. The outcomes are astonishing.

Machine learning algorithms are classified into 4 types:

- Supervised and
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning

## What Are The 10 Popular Machine Learning Algorithms?

Below is the list of Top 10 commonly used Machine Learning (ML) Algorithms:

- Linear regression
- Logistic regression
- Decision tree
- SVM algorithm
- Naive Bayes algorithm
- KNN algorithm
- K-means
- Random forest algorithm
- Dimensionality reduction algorithms
- Gradient boosting algorithm and AdaBoosting algorithm

Consider how you would arrange a set of random wood logs in ascending weight order to get a sense of how this method works. The drawback is that you can’t weigh every log. You must visually analyze the height and girth of the log to determine its weight, then arrange it using a combination of these observable factors. This is how machine learning’s linear regression works.

By fitting the independent and dependent variables to a line, a connection between them is created. The linear equation Y=a*X+b describes this line, which is referred to as the regression line.

There are lots of Machine Learning Algorithms are Avaiable.

**Linear regression**

When solving for:

The dependent variable is Y. Slope X is an independent variable, while b is the intercept. By minimizing the sum of the squared difference of the distance between the data points and the regression line, coefficients a and b are produced.

**Logistic regression**

To estimate discrete values (often binary values like 0/1) from a collection of independent variables, logistic regression is utilized. By adjusting the data to a logit function, it aids in predicting the likelihood of an occurrence. Additionally known as logit regression.

The techniques described below are often used to enhance logistic regression models: put interaction phrases in remove components regularize procedures make use of a nonlinear model

**Decision tree**

One of the most widely used machine learning algorithms nowadays is the decision tree algorithm; it is a supervised learning method used to categorize situations. For both categorical and continuous dependent variables, it performs well when categorizing. The population is divided into two or more homogenous sets using this approach based on the most important characteristics or independent variables.

**SVM algorithm**

Using the SVM technique, you may classify data by plotting the raw data as dots in an n-dimensional space (where n is the number of features you have). The data may then be easily classified since each feature’s value is then connected to a specific coordinate. The data may be divided into groups and shown on a graph using lines known as classifiers.

**Naive Bayes Algorithm**

An assumption made by a Naive Bayes classifier is that the existence of one feature in a class has no bearing on the presence of any other features.

A Naive Bayes classifier would take into account each of these characteristics individually when determining the likelihood of a certain result, even if these attributes are connected to one another.

A Naive Bayesian model is simple to construct and effective for large datasets. It is known to perform better than even the most complex categorization techniques while being basic.

**KNN algorithm**

Problems involving classification and regression may both be solved using this approach. It seems that the solution of categorization issues is increasingly often applied within the Data Science business. It is a straightforward algorithm that sorts new instances by getting the consent of at least k of its neighbors and then saves all of the existing cases. The class with which the case has the most characteristics is then given a case. This calculation is made using a distance function.

KNN is simple to comprehend when compared to reality. For instance, it makes sense to speak with a person’s friends and coworkers if you want to learn more about them! Before choosing the K Nearest Neighbors algorithm, take the following into account: The computational cost of KNN is high. Higher range variables should be standardized to prevent algorithm bias. Preprocessing of the data is still necessary.

**K-means**

It is a technique for unsupervised learning that addresses clustering issues. Data sets are divided into a certain number of clusters—call let’s it K—in such a manner that each cluster’s data points are homogeneous and distinct from those in the other clusters.

K-means cluster formation process :

For each cluster, the K-means algorithm selects k centroids, or points. With the nearest centroids, or K clusters, each data point forms a cluster. Now, new centroids are produced depending on the cluster members already present. The closest distance for each data point is calculated using these updated centroids. Till the centroids stay the same, this procedure is repeated.

**Random forest algorithm**

The term “Random Forest” refers to a collection of decision trees. Each tree is assigned a class, and the tree “votes” for that class, in order to categorize a new item based on its characteristics. The categorization with the highest votes is selected by the forest (over all the trees in the forest).

Every tree is cultivated & planted as follows:

If the training set has N instances, then a random sample of N cases is chosen. This sample will serve as the tree’s training set.

m variables are randomly chosen from the M input variables at each node, and the best split on this m is utilized to divide the node, if there are M input variables. Throughout this procedure, the value of m is kept constant.

Each tree is developed to its fullest potential. Pruning is not done.

**Dimensionality reduction algorithms**

In the modern world, businesses, governments, and research institutions store and analyze enormous volumes of data. As a data scientist, you are aware that there is a wealth of information included in this raw data; the difficult part is spotting important patterns and variables.

You may identify pertinent information by using dimensionality reduction methods like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest.

**Gradient boosting algorithm and AdaBoosting algorithm**

These boosting techniques are used for handling enormous amounts of data in order to create very accurate predictions. Boosting is an ensemble learning approach that increases resilience by combining the predictive strength of numerous base estimators.

In other words, it builds a powerful predictor by combining a number of weak or average predictors. These boosting algorithms consistently do well in contests for data science, such as Kaggle, AV Hackathon, and CrowdAnalytix. These are currently the most popular machine learning algorithms. Use them in conjunction with Python and R codes to get precise results.