The technological revolution has penetrated the industry. Irrespective of the industrial size, the significance of Machine Learning and data is evident. There has also been a global rise in the Machine Learning market size. In 2021 the Machine Learning market size was around USD 15.47 billion, and it is expected to reach around USD 305.62 billion by 2030. The segment will witness a CAGR of 39.3% between 2022 and 2030. This will again give rise to a higher demand for Machine Learning experts. While the role of Machine Learning experts is inevitable, but the tools used by them also play an important role. In this blog, we will explore some of the popular machine learning algorithm widely used by professionals.
What is Machine Learning Algorithm?
A Machine Learning algorithm is a set of instructions that a computer follows to learn from data and improve its performance on a specific task. These algorithms improvise their functionality as and when new information enters the system.
Different types of Machine Learning algorithms are focused on deriving useful insights. These are used to resolve various problems like image recognition, voice recognition, Natural Language Processing, forecasting and others.
Types of Machine Learning Algorithms
Supervised Learning Algorithms
These algorithms work on labelled data. Here, the correct output is provided for each. It helps in predicting new examples from the same distribution as the training sets.
Unsupervised Learning Algorithms:
These are not given any labelled training examples. On the contrary, the professional must unfold the underlying structure of the data using different techniques like clustering.
Semi-supervised Learning Algorithms
These are a mix of supervised and unsupervised learning where they are offered or a combination of labelled and unlabelled data. These are more effective, especially when label data is less.
Reinforcement Learning Algorithms
These work by interacting with the environment and receiving rewards or punishments for their actions. It works on controlling autonomous systems.
Deep Learning Algorithms
These are inspired by the brain’s structure and function, especially the human brain’s neural network. These algorithms are more complex and have multiple layers of artificial neural networks. These are helpful in image and speech recognition.
Use Cases of Machine Learning Algorithm
The impact of Machine Learning across the different industrial spectrum is evident. Some of the popular application of this technology includes the following:
Image and Speech Recognition
One prominent application is speech recognition. The algorithms find application in identifying objects, people, images and scenes. These algorithms are also powerful enough to transcribe spoken words.
Natural Language Processing
These algorithms are powerful in interpreting human language and enable applications like text summarization.
Predictive Modelling
You can also use an ML algorithm to analyze historical data and predict future events, stock market pricing, or cryptocurrency value.
Fraud Detection
Machine Learning algorithms can identify suspicious activity and flag it for further investigation.
Personalization
One of the prominent applications of Machine Learning is in customizing and personalizing recommendations to make them more user-specific. For example, e-commerce platforms use these algorithms to make product recommendations.
Healthcare
Machine Learning algorithm plays a vital role in the healthcare segment for analysis of medical data and predicting patient outcomes or potential outbreaks of diseases like the one used in the case of COVID.
Agriculture
Data from the sensors can be analysed to predict crop yields. And also improve resource efficiency.
Finance
Machine Learning algorithms analyzes financial data, predict market movements, or identify fraudulent activity.
5 Machine Learning Algorithm and Its Key Features
Linear Regression
It is a form of supervised learning used to predict the value of one variable based on the value of another variable. These two variables are called dependent and independent.
Some of Its Significant Features Are:
Its output is a continuous value
It highlights a linear relationship between input features and the output
It also reduced the errors between the actual and predicted value
Application of Linear Regression:
Predicting Stock Prices: One of the prominent applications of linear regression is to predict the relationship between historical stock prices and other relevant factors, like the company’s performance and the economy’s overall state.
Sales Forecasting: This algorithm helps predict the future based on past data and other intervening factors like marketing campaigns.
House Price Prediction: The house’s sale price prediction is also one of the applications of linear regression. This algorithm considers factors like the size of the house, location and other parameters that can impact the valuation of the house.
Logistic Regression
It is also a supervised learning type that applies a logistic curve to the data. It is used to estimate the probability of the occurrence of an event. There are three types of logistic regression, binary, ordinal and multinomial.
Some of Its Significant Features Are:
- The output value in logistic regression is either 0 or 1.
- It predicts the probability of a given example
- It also minimizes the log loss between the predicted probability and the actual class
Application of Logistic Regression:
Fraud Detection: Organizations are looking for tools that can reduce fraud. With logistic regression, you can identify suspicious activity in financial transactions. It unfolds the relationship between the transaction details and the likelihood that it is fraudulent.
Customer Churn Prediction: Companies wanting to know the likelihood that a customer will stop using a company’s products or services can find this application helpful. It works on past behaviour and other relevant factors.
Disease Diagnosis: The healthcare segment actively relies on Machine Learning to predict the likelihood of a patient encountering a particular disease. This method incorporates the study of symptoms and test results.
K-Means Clustering
It is an unsupervised learning algorithm for dividing unlabelled data into clusters. Here K means several pre-defined clusters created as a part of the process. For example, if K=2, there will be two clusters, and if K = 3, there will be three clusters.
Some of Its Significant Features Are:
- One doesn’t need prior knowledge of the class labels
- Advance declaration of Clusters or K must
- It is sensitive to the initial placement of the centroids
Application of K-means Clustering:
Document Classification: One can easily cluster documents into categories like tags, topics, and content. The documents are represented as a vector, using term frequency to identify the commonly used terms.
Identifying Crime Localities: The algorithm can identify crimes in a location based on the information related to crimes taken place in the city.
Image compression: K-means clustering can be used to reduce the number of colours in an image, resulting in a smaller file size and faster loading times..
Decision Trees
This is a popular supervised learning algorithm that works for classification and regression tasks. As evident from the name, it operates by constructing a tree-like decision model based on the data’s features.
Some of Its Significant Features Are:
- Easy to understand
- Work well with categorical and numerical data
- Classification and regression task
Application of Decision Trees
Fraud Detection: It finds applications for detecting fraudulent activity. This algorithm works by studying the past data available.
Customer Segmentation: One of the prime focuses of organisations is to aggregate the customers based on age, income and preferences. Decision trees are helpful in this.
Marketing: One can easily predict which customers are most likely to respond to a marketing campaign and identify the factors most important in determining their response.
Closing Thoughts
These are some of the common Machine Learning algorithms used by professionals. This algorithm plays a significant role in simplifying the task of Machine Learning experts and coming to a defined conclusion.
