Top 5 Machine Learning Algorithms For Beginners

Top 5 Machine Learning Algorithms For Beginners


Machine learning algorithms can let machines do surgery, play chess, and become smarter and more human-like. They are evolving in a world where almost all tasks that were once done by hand are now mechanized. The democratization of computing tools and methods is one of the key aspects of this revolution that stands out. By smoothly implementing cutting-edge methodologies, data scientists have also developed sophisticated data-crunching machines during the last five years. The outcomes are astonishing.

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Categories for machine learning algorithms:

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

Popular machine learning algorithms are

1. Linear Regression

Consider how you would arrange a set of random wood logs in ascending weight order to understand how this algorithm functions. The drawback is that you can’t weigh every log. It would help if you visually analyzed the height and width of the log to determine its weight and then arrange it using a combination of these observable factors.

A connection between them is made by checking the independent and dependent variables to a line. The linear equation Y=a*X+b describes this line as the regression line.

2. Logistic Regression

Logistic regression is utilized to estimate discrete values (often binary ones like 0/1) from a set of independent variables. Adjusting the data to a logit function aids in indicating the chance of an outcome. It is likewise characterized as logit regression.

To improve logistic regression models:

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

3. Decision Tree

One of the most popular machine learning algorithms today is the decision tree algorithm, a supervised learning method used to categorize situations. For both categorical and continuous dependent variables, it performs well when indexing. The population is divides into two or more homogeneous sets based on the most important characteristics or independent variables. 

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  1. Support-Vector-Machine (SVM) Algorithm

Using the SVM algorithm, you can 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 be easily defines because each part’s value is fuses to a specific coordinate. The data can be divides into groups and plotted on a graph using lines known as classifiers.

5. K-Means

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

K-means cluster formation process

  • For every cluster, the K-means algorithm selects k centroids or points.
  • Each data point forms a cluster with the nearest centroids or K clusters.
  • Now, new centroids are devices on the existing cluster members.
  • The closest distance for each data point is calculate using these unique centroids. Till the centroids stay the same, this process continues.


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