Discrete values are estimated using logistic regression.

The manual definition is shifting in a world where practically all manual operations are automated. Computers can use Machine Learning algorithms to help them play chess, do surgery, and improve their intelligence and personalisation.

We live in an era of rapid technological innovation, and we can predict what will happen in the future by looking at how computers have evolved.

Data scientists have constructed sophisticated data-crunching machines by effortlessly performing modern techniques in the last five years. The outcomes have been spectacular. The democratisation of computer tools and procedures is one of the most remarkable characteristics of this revolution.

Algorithms for machine learning can be categorized into four types:

  • Supervised Machine Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

These four, however, are divided into other categories.

Popular Machine Learning Algorithms

Linear Regression

Consider how you would arrange random wood logs to increase their weight to see how this algorithm works. But there’s a catch: you can’t weigh each log. You must estimate its weight based on the log’s height and girth (visual analysis) and arrange it based on a combination of these visible criteria. This is how machine learning works with linear regression.

By fitting independent and dependent variables to a line, a relationship between them is established in this procedure. The regression line is defined as Y= an *X + b, representing a linear equation.

In this equation:

Y – Dependent Variable

a – Slope

X – Independent variable

b – Intercept

By minimizing the squared difference of distance between data points and the regression line, the coefficients, a and b, are calculated.

Logistic Regression

Logistic regression estimates discrete values (typically binary values like 0/1). It helps anticipate the probability of an event by fitting data to a logit function. It’s also known as Logit Regression. These techniques are widely employed to help enhance logistic regression models:

  • interaction terms
  • eliminate features
  • regularize techniques
  • use a non-linear model

Decision Tree

The Decision Tree method is one of the most widely used machine learning algorithms today; it is a Supervised Machine Learning learning approach for classifying issues. It works well for categorizing both continuous and categorical dependent variables. We split the population into two or more homogeneous groups using this strategy based on the most critical attributes/independent variables.

SVM (Support Vector Machine) Algorithm

Classifiers are lines that can separate data and plot it on a graph. Raw data are represented as points in n-dimensional space using the SVM algorithm, a classification approach. Each feature’s value is linked to a specific coordinate, making data classification simple.

Naive Bayes Algorithm

The existence of one feature in a class is assumed to be independent of the presence of any other part by a Naive Bayes classifier.

Even if these characteristics are connected, a Naive Bayes classifier will analyze each of them separately when determining the likelihood of a specific outcome.

A Naive Bayesian model is simple to construct and can analyze large datasets. It’s easy to use and has been shown to outperform even the most complex categorization systems.

KNN (K- Nearest Neighbors) Algorithm

This method can be used to tackle classification and regression difficulties. It looks to be being more widely used in the Data Science industry to solve categorization problems. It’s a straightforward algorithm that saves all existing examples and classifies any new ones based on the votes of its k neighbours. The case is then placed in the class that shares the most similarities. A distance function is used to complete this measurement.

It becomes evident when comparing KNN to actual life. For example, if you want to learn more about a person, you should speak with their friends and coworkers! Consider the following factors before deciding on the K Nearest Neighbors Algorithm:

  • The KNN algorithm is computationally intensive.
  • The procedure will be distorted if higher range variables are not adjusted.
  • Pre-processing of data is still required.


It is a clustering problem-solving unsupervised learning algorithm. Data sets are divided into a certain number of clusters so that all data points within each cluster are homogeneous and distinct from data in other groups.

K-means creates clusters in the following way:

  • The K-means algorithm selects k centroids, or points, for each cluster.
  • Each data point forms a cluster with the closest centroids, resulting in K clusters.
  • It now generates new centroids based on the existing cluster members.

The closest distance between each data point is determined using these new centroids. This cycle is repeated until the centroids remain unchanged.

Random Forest Algorithm

A Random Forest is a collection of decision trees that have been arranged in a non-random order. Each tree is classed, and the tree “votes” for that class to classify a new item based on its attributes. The forest chooses the classification with the highest votes is selected by the forest (over all the trees in the woods).

The following is how each tree is planted and grown:

  • If the training set contains N cases, a sample of N cases is selected randomly. This sample will serve as the tree’s training set.
  • A number m is the node if there are M input variables. Throughout this operation, the value of m is kept constant.
  • Each tree is pruned and grown to its full potential. Pruning is not an option.

Dimensionality Reduction Algorithms

Corporations, government agencies, and research organizations store and analyze large volumes of data in today’s environment. As a data scientist, you’re well aware that this raw data includes a wealth of information; the trick is discovering relevant patterns and variables.

Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest are dimensionality reduction methods to help you identify important details.

Gradient Boosting Algorithm and AdaBoosting Algorithm

These are boosting techniques employed when large amounts of data must be processed to create accurate predictions. Growing is an ensemble learning approach that improves robustness by combining the predictive power of numerous base estimators.

To put it another way, it combines several weak or mediocre predictors to create a strong predictor. These boosting methods consistently perform well in data science competitions like Kaggle, AV Hackathon, and CrowdAnalytix. These are today’s most popular machine learning algorithms. Use them in conjunction with Python and R Codes to get precise results.


Start now if you wish to pursue a career in machine learning. The area is expanding, and the sooner you grasp the reach of machine learning techniques, the quicker you’ll be able to solve challenging word problems. Suppose you have prior expertise in the field and wish to advance your career. In that case, you can enrol in the Post Graduate Program in AI and Machine Learning, which is offered in collaboration with Purdue University and IBM. This course will teach you Python, the Tensor Flow Deep Learning algorithm, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.

If you’d like to learn more about how businesses can use machine learning algorithms, contact the ONPASSIVE team for more info.