5 Machine Learning Algorithms That Every ML Engineer Should Know

Summary: Machine Learning algorithms are changing the game across industries. From Netflix recommendations to fraud detection, they analyze data to make predictions and solve problems. They power image recognition in self-driving cars and medical diagnosis tools. Businesses use them for customer segmentation, and scientists leverage them for discoveries. Get ready for even more innovation!

Introduction

The technological revolution has penetrated every 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 was at USD 15.47 billion, and will reach USD 305.62 billion by 2030.

The segment will witness a CAGR of 39.3% between 2022 and 2030. Consequently, this has led to a higher demand for Machine Learning experts. The success of a Machine Learning professional also depends on the apt use of the ML algorithm.

In this blog, we will explore some of the popular machine learning algorithms that are widely used by professionals.

What is a Machine Learning Algorithm?

A Machine Learning algorithm is a set of instructions 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 for image recognition, voice recognition, Natural Language Processing, forecasting and others. 

Types of Machine Learning Algorithms

Machine learning algorithms can be broadly categorized into three main types: supervised, unsupervised, and reinforcement learning. Each type is suited for different tasks and problems.

Supervised Learning

This algorithm is trained on labelled data, meaning the data has already been categorized or classified. Supervised learning algorithm learns to identify patterns and relationships between the input data (features) and the desired output (labels). Once trained, the algorithm can  used to make predictions on new, unseen data. Common supervised learning tasks include:

Classification: Classifying data points into predefined categories, such as spam detection or image recognition.

Regression: Predicting continuous values, such as weather forecasting or stock prices. 

Unsupervised Learning

In unsupervised learning, the algorithm given unlabeled data, which means the data has no predefined categories or classifications. The algorithm’s task is to find hidden patterns or structures within the data. Common unsupervised learning tasks include:

Clustering: Grouping data points into clusters based on similarities, such as customer segmentation or anomaly detection.

Dimensionality reduction: Reducing the number of features in a dataset while preserving the most important information. 

Reinforcement Learning

In reinforcement learning, the algorithm interacts with an environment and learns through trial and error. The algorithm receives rewards for making good decisions and penalties for making bad decisions. Over time, the algorithm learns to make choices that maximize its reward. Reinforcement learning used in applications such as:

Robotics: Training robots to perform tasks in complex environments.

Game playing: Training AI agents to play games at a superhuman level.

Use Cases of Machine Learning Algorithm

The impact of Machine Learning across the different industrial spectrum is evident. Some of the popular applications of this technology include the following:

Recommendations

Machine Learning is a key technology behind recommendation systems used by companies like Netflix and Amazon. These algorithms analyze your past behaviour, such as what movies you’ve watched or products you’ve purchased, to suggest new items you might be interested in.

Fraud Detection

Banks and financial institutions use Machine Learning to identify fraudulent transactions. These algorithms can analyze patterns in spending habits to flag suspicious activity, such as a large purchase made from an unfamiliar location.

Image Recognition

Machine Learning used in image recognition software to identify objects and people in images. This technology  use in a variety of applications, such as facial recognition for security purposes and self-driving cars.

Medical Diagnosis

Machine Learning algorithms can traine to analyze medical data, such as X-rays and blood tests, to help doctors diagnose diseases. This can improve the accuracy and speed of diagnosis.

Customer Segmentation

Businesses can use Machine Learning to segment their customers into different groups based on their demographics, interests, and purchasing behaviour. This allows them to tailor their marketing campaigns to specific customer groups.

Scientific Discovery

Machine Learning  used in scientific research to analyze large datasets and identify patterns that would be difficult for humans to find. This is helping to accelerate scientific discovery in fields such as astronomy and genetics.

These are just a few examples of the many ways Machine Learning algorithms are being used today. As Machine Learning technology continues to develop, we can expect to see even more innovative applications emerge in the future. 

5 Machine Learning Algorithm and Its Key Features 

1. 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.

Significant Features

Its output is a continuous value

It highlights a linear relationship between input features and output

It also reduced the errors between the actual and predicted value

Application

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 prediction of the house’s sale price 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.

2. 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.

Significant Features

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

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 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.

3. 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; if K = 3, there will be three.  

Significant Features

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

Document Classification: One can easily cluster documents into categories like tags, topics, and content. The documents 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 in the city.

Image compression: K-means clustering can reduce the number of colours in an image, resulting in a smaller file size and faster loading times.

4. 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.

Significant Features

Easy to understand

Work well with categorical and numerical data

Classification and regression task

Application

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.

5. Support Vector Machine (SVM)

SVMs are powerful supervised learners that find the best dividing line (hyperplane) to classify data points into categories. They excel in high dimensions and with limited data, making them a versatile tool for various classification tasks.

Significant Features

Finds a hyperplane that best separates data points belonging to different classes.

Effective in high-dimensional spaces and with small datasets.

Can also be used for regression tasks.

Less interpretable compared to simpler models like linear regression.

Application

Image Recognition: SVMs can be trained to distinguish between objects in images. For example, an SVM could be used to identify faces in photos or classify different types of products in an image.

Spam Detection: Email providers can use SVMs to analyze emails and classify them as spam or not spam. The SVM can learn from past emails what characteristics typically indicate spam and use that knowledge to filter new emails.

Text Classification: SVMs can be used to categorize text documents. For instance, news articles could be classified by topic (sports, politics, business) or reviews could be classified as positive, negative, or neutral.

Frequently Asked Questions

What Are The Different Types of Machine Learning Algorithms?

There are three main categories: supervised learning (trains on labelled data for tasks like classification or prediction), unsupervised learning (finds patterns in unlabeled data for tasks like grouping or dimensionality reduction), and reinforcement learning (learns through trial and error in an interactive environment).

How Do I Choose The Right Algorithm for My Project?

The best algorithm depends on your data and task. Supervised learning is good for classification and prediction, while unsupervised learning is for exploring unlabeled data. Consider factors like data size, interpretability, and desired outcomes when choosing an algorithm.

Are Machine Learning Algorithms Like a Black Box?

Some algorithms can be complex, but interpretability is an active area of research. Many algorithms offer ways to understand their decision-making process. You can also choose algorithms known for their relative transparency. 

Closing Thoughts

These are some of the common Machine Learning algorithms used by professionals. This algorithm plays a significant role in simplifying the tasks of Machine Learning experts and coming to a defined conclusion.

If you are looking forward to making a career as a machine learning expert, joining the free ML101 course by Pickl.AI will help you build a strong conceptual foundation. For more information, log on to Pickl.AI today.