In computer, Algorithm is defined as a set of rules to solve calculations or some kind of problem-solving operation, especially by a computer. Most commonly, the Algorithm is used by programmers or developers to solve problems while developing computer software and applications. In the process of creating an Algorithm, an infinite number of ways can be generated.
Here’s the list of different scenarios where we can apply Algorithms:
- Naive Bayes Classifier Algorithm,
- K-Means Clustering Algorithm,
- Support Vector Machine (SVM) Learning Algorithm,
- Recommender System Algorithm,
- Linear Regression,
- Logistic Regression,
- Decision Tree Machine Learning Algorithm,
- Random Forest ML Algorithm,
- Principal Component Analysis (PCA) Algorithm,
- Artificial Neural Networks,
- K-Nearest Neighbors Algorithm.
1 – Naive Bayes Classifier Algorithm
If we’re planning to automatically classify web pages, forum posts, blog snippets, and tweets without manually going through them, then the Naive Bayes Classifier Algorithm will make our life easier.
This classifies words based on the popular Bayes Theorem of probability and is used in applications related to disease prediction, document classification, spam filters, and sentiment analysis projects.
We can use the Naive Bayes Classifier Algorithm for ranking pages, indexing relevancy scores and classifying data categorically.
2 – K-Means Clustering Algorithm
K-Means Clustering Algorithm is frequently used in applications such as grouping images into different categories, detecting different activity types in motion sensors and for monitoring whether tracked data points change between different groups over time. There are business use cases of this algorithm as well such as segmenting data by purchase history, classifying persons based on different interests, grouping inventories by manufacturing and sales metrics, etc.
The K-Means Clustering Algorithm is an unsupervised Machine Learning Algorithm that is used in cluster analysis. It works by categorizing unstructured data into a number of different groups ‘k’ being the number of groups. Each dataset contains a collection of features and the algorithm classifies unstructured data and categorizes them based on specific features.
3 – Support Vector Machine (SVM) Learning Algorithm
Support Vector Machine Learning Algorithm is used in business applications such as comparing the relative performance of stocks over a period of time. These comparisons are later used to make wiser investment choices.
SVM Algorithm is a supervised learning algorithm, and the way it works is by classifying data sets into different classes through a hyperplane. It marginalizes the classes and maximizes the distances between them to provide unique distinctions. We can use this algorithm for classification tasks that require more accuracy and efficiency of data.
4 – Recommender System Algorithm
The Recommender Algorithm works by filtering and predicting user ratings and preferences for items by using collaborative and content-based techniques. The algorithm filters information and identifies groups with similar tastes to a target user and combines the ratings of that group for making recommendations to that user. It makes global product-based associations and gives personalized recommendations based on a user’s own rating.
For example, if a user likes the TV series ‘The Flash’ and likes the Netflix channel, then the algorithm would recommend shows of a similar genre to the user.
5 (i) – Linear Regression
Linear Regression widely used for applications such as sales forecasting, risk assessment analysis in health insurance companies and requires minimal tuning. It is basically used to showcase the relationship between dependent and independent variables and show what happens to the dependent variables when changes are made to independent variables.
5 (ii) – Logistic Regression
Logistic regression is used in applications such as:
- To Identifying risk factors for diseases and planning preventive measures,
- Classifying words as nouns, pronouns, and verbs,
- Weather forecasting applications for predicting rainfall and weather conditions,
- In voting applications to find out whether voters will vote for a particular candidate or not.
A good example of logistic regression is when credit card companies develop models which decide whether a customer will default on their loan EMIs or not. The best part of logistic regression is that we can include more explanatory (dependent) variables such as dichotomous, ordinal and continuous variables to model binomial outcomes.
Logistic Regression is a statistical analysis technique which is used for predictive analysis.
6 – Decision Tree Machine Learning Algorithm
Applications of this Decision Tree Machine Learning Algorithm range from data exploration, pattern recognition, option pricing in finances and identifying disease and risk trends.
We want to buy a video game DVD for our best friend’s birthday but aren’t sure whether he will like it or not. We ask the Decision Tree Machine Learning Algorithm, and it will ask we a set of questions related to his preferences such as what console he uses, what is his budget. It’ll also ask whether he likes RPG or first-person shooters, does he like playing single player or multiplayer games, how much time he spends gaming daily and his track record for completing games.
Its model is operational in nature, and depending on our answers, the algorithm will use forward, and backward calculation steps to arrive at different conclusions.
7 – Random Forest ML Algorithm
The random forest algorithm is used in industrial applications such as finding out whether a loan applicant is low-risk or high-risk, predicting the failure of mechanical parts in automobile engines and predicting social media share scores and performance scores.
The Random Forest ML Algorithm is a versatile supervised learning algorithm that’s used for both classification and regression analysis tasks. It creates a forest with a number of trees and makes them random. Although similar to the decision trees algorithm, the key difference is that it runs processes related to finding root nodes and splitting feature nodes randomly.
It essentially takes features and constructs randomly created decision trees to predict outcomes, votes each of them and consider the outcome with the highest votes as the final prediction.
8 – Principal Component Analysis (PCA) Algorithm
PCA algorithm is used in applications such as gene expression analysis, stock market predictions and in pattern classification tasks that ignore class labels.
The Principal Component Analysis (PCA) is a dimensionality reduction algorithm, used for speeding up learning algorithms and can be used for making compelling visualizations of complex datasets. It identifies patterns in data and aims to make correlations of variables in them. Whatever correlations the PCA finds is projected on a similar (but smaller) dimensional subspace.
9 – Artificial Neural Networks
Essentially, deep learning networks are collectively used in a wide variety of applications such as handwriting analysis, colorization of black and white images, computer vision processes and describing or captioning photos based on visual features.
Artificial Neural Network algorithms consist of different layers which analyze data. There are hidden layers which detect patterns in data and the greater the number of layers, the more accurate the outcomes are. Neural networks learn on their own and assign weights to neurons every time their networks process data.
Convolutional Neural Networks and Recurrent Neural Networks are two popular Artificial Neural Network Algorithms.
Convolutional Neural Networks are feed-forward Neural networks which take in fixed inputs and give fixed outputs. For example – image feature classification and video processing tasks. Recurrent Neural Networks use internal memory and are versatile since they take in arbitrary length sequences and use time-series information for giving outputs.
For example – language processing tasks and text and speech analysis.
10 – K-Nearest Neighbors Algorithm
KNN algorithm is used in industrial applications in tasks such as when a user wants to look for similar items in comparison to others. It’s even used in handwriting detection applications and image/video recognition tasks.
The best way to advance our understanding of these algorithms is to try our hand in image classification, stock analysis, and similar beginner data science projects.
Also read – What is a Quantum Computer?