Machine Learning Algorithms - Know the Essentials

By Nandini

Last updated on Feb 24 2022

Machine Learning Algorithms  - Know the Essentials

A Complete Guide to Machine Learning Algorithms


We are living in the exciting time of making reality out of science fiction. Humans have always made mistakes, but learned from them and worked on them, and hence Mankind leaped a giant leap. Now even this noble attribute is delegated to machines, resulting in one of the most fascinating breakthroughs in Technology. Machine Learning Algorithms are Computer Algorithms that can improve automatically based on experience and input data.

Algorithms are nothing but a set of instructions to accomplish a task or simply the number of steps sequentially involved in a particular function. The task could be anything from making tea to calculating the projectile of an artificial satellite. Any task can be broken down into an algorithm. Below is an example of an algorithm for making Tea while I leave it to your imagination the algorithm involving the artificial satellite.

Step 1 - Boil Water

Step 2 – Pour it in a cup

Step 3 – Place the Teabag

Step 4 – Add sugar and stir

The algorithm mentioned above would not necessarily satisfy everyone. Some might like to add sugar before the teabag. Some might even want to add the tea bag while the water is boiling. So there’s a need for a new algorithm to be written for every time a type of tea is made, but with Machine Learning Algorithms, the Machine/Algorithm will itself create a new logic for the data you provide. Making the changes based upon the Response and experience.

Machine Learning Algorithm is an upgrade of the regular algorithms, and it enables your program to make smart decisions by itself. Machine Learning algorithms are mainly divided into two Phases,

  • Training Phase
  • Testing Phase

Training Phase - In this Phase all the relevant data set is fed into the application to recognize the pattern and perform based on certain criteria.

Testing Phase- In this Phase the application will develop a logic based on the computation you provided during the training phase and provide results.


Types of Machine Learning Algorithms


Deep Learning Algorithms are used to teach computers algorithmic functions that mirror the human brain, by using a large amount of data. Machine learning Algorithms are further classified into three types based on Deep Learning

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement


Supervised Machine Learning

Supervised Learning is an algorithm that continuously maps an input to an output based on the standard input-output pair. Here the Algorithm result is continuously corrected till it produces the desired outcome.

Supervised Learning is much like a teacher teaching their student since the algorithm is trained by a labelled set of data and takes direct feedback to check if the prediction is correct or not.

Examples of supervised Machine Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • KNN
  • Naive Bayes


Unsupervised Machine Learning

Unsupervised Machine Learning is an algorithm where the system learns to create a pattern using untagged data. Here the Machine is forced to build a compact internal representation of the world and then generate content from it.

Unlike Supervised Machine Learning, there is no continuous Correction or Teacher guidance. Here the algorithm detects patterns based on the input data.

Examples of Unsupervised Machine Learning Algorithms

  • K-Means Clustering
  • Principle Component Analysis
  • Hierarchal Clustering


Reinforcement Learning

Reinforcement Learning is about taking suitable action to maximize the reward in a particular situation. In other words, Reinforcement Learning Algorithm is trained to take positive action that would be beneficial and omit actions that would be detrimental to the overall outcome.

The Machine gains a reward point if the response is correct and a penalty point if the response is wrong. Reinforcement learning is achieved by maximizing the interaction between the environment and the system.


List of Commonly Used Machine Learning Algorithms
  1. Linear Regression
  2. Logistic Regression
  3. Decision Tree
  4. Naive Bayes
  5. K-NN


Linear Regression Machine Learning

Linear Regression in Machine Learning is one of the easiest Algorithms, it is a statistical method used to predict analysis. Linear regression algorithm shows the relationship between a dependant variable (Y) and one more independent variable (X), hence known as linear regression.

Linear regression is used to predict sales, weather, and any problem involving simple dependant and independent variables.


Logistic Regression

Logistic Regression is a statistical analysis method to predict binary outcomes such as win/lose, yes/no, or Dead/Alive. The logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent data variables. It allows the algorithms to classify the incoming data based on historical data.


Decision Tree

A Decision Tree is a supervised learning technique mostly used for classification problems even though it can be both used for classification and regression problems.

A Decision Tree is a pictorial representation of an inverted tree where it braches to output either satisfying a condition or not.


Naive Bayes

Naïve Bayes is a collection of classification algorithms based on the Bayes theorem. It is used for categorical input variables compared to numerical variables.

Bayes theorem assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. With Naive Bayes it is easy to predict the class of the Data set and also multi-class prediction.


K-NN (K- Nearest Neighbours)

It is a simple algorithm used to solve both classification and regression-based problems. K-NN stores all the historic data and cases and assigns the new incoming data to the pre-existing set based on similarity.

K-NN is disadvantageous when the data set is too large to handle.



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About the Author

Sprintzeal   Nandini

Nandini has over 3 years of experience in creating informative, authentic, and engaging content. She is a technical content writer who is skilled in writing well-researched articles, blog posts, newsletters, and other forms of content. Her works are focused on the latest updates in E-learning, professional training and certification, and other important fields in the education domain.

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