How have Machine Learning Algorithms changed in the past year?

Vipinraj Nair
7 min readJun 28, 2021


Machine Learning (ML) has grown tremendously over the past few years. This technology has made an immense impact in almost every industry out there. People often use Artificial Intelligence (AI) and Machine Learning (ML) interchangeably, but they are not the same. Machine learning is one of the most growing fields and a way to achieve AI.

Machine Learning has already changed the way we interact, and it will change the way we live our lives in the future. For that matter, ML is the technology behind chatbots, predictive text, language translation apps, and more. The shows Netflix suggests you and the social media feeds presented to you use ML technology. Thus, you are already using the technology in your day-to-day lives.

When companies deploy artificial intelligence programs today, they are often using machine learning. Thus, the terms are used ambiguously. To be precise, Machine learning (ML) is a subfield of Artificial Intelligence (AI) that gives computers the ability to learn without being programmed explicitly.

Machine learning is a rapidly growing technology that every business will encounter sooner or later. Hence, knowing about it and gaining knowledge in ML is mandatory to stay ahead in business.

A 2020 survey says that 67% of companies are using machine learning while 97% are planning to use it in the next year.

All industries including, manufacturing, retail, banking, and even bakeries, use ML to offer better services to their customers. Thus, ML will change the way businesses from every industry operate. So, it is crucial to engage and start understanding these tools and how to use them effectively.

What is Machine Learning?

Machine learning is a type of Artificial Intelligence (AI) that enables the machine to imitate intelligent human behavior. Hence, ML allows software applications to become more precise at predicting outcomes without being programmed explicitly.

Machine learning algorithms use history data as input to figure out the new output values. For instance, medical diagnosis, image processing, and other intelligent systems that use ML algorithms can learn from experience or historical data.

Machine Learning is all about letting computers learn to program themselves through experience. ML starts with data like numbers, photos, or text, bank transactions, pictures of people, repair records, or sales records. The gathered data is the training data or the information that you can use to train the ML model. The more data, the better model.

Programmers choose a machine learning model to supply the data and let the computer model train itself to find patterns or make predictions. The programmer can also tweak the model eventually, including changing its parameters to achieve better results.

Machine Learning Categories

1) Supervised Machine Learning Models

You can train these models with labeled data sets that enable the models to learn and grow more accurately over time. For example, with an algorithm, you can train images of birds, all labeled by humans, and the machine would learn ways to identify images of birds on its own. Supervised machine learning is more common in use today.

2) Unsupervised Machine Learning Models

In unsupervised machine learning, the program looks for patterns in unlabeled data. The program can find patterns or trends that people don’t look for explicitly. For example, an unsupervised machine learning program can see through online sales data and identify various types of clients making purchases.

3) Reinforcement Machine Learning Models

Reinforcement machine learning focuses on training machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by dictating the machine to make the right decisions. Thus, the machine will learn to take appropriate actions eventually.

Machine Learning (ML) is the technology behind several subfields of Artificial Intelligence (AI) like:

1) Natural Language Processing

In this machine learning field, machines learn to understand the natural languages of humans instead of the generally used data and numbers in computer programming. It allows machines to recognize the language, understand it and respond to it. Machines can even create new text and translate it between languages. Natural language processing enables popular technology like chatbots and digital assistants like Siri and Alexa.

2) Neural Networks

Neural networks are one of the most popular machine learning algorithms. Artificial neural network models are similar to a human brain, with millions of processing nodes interconnected and organized into layers.

In an artificial neural network, cells or nodes are connected with each cell processing inputs, producing an output that is sent to other neurons. Labeled data moves through the nodes or cells, with each cell performing a separate function.

In a neural network, different nodes will access the information and produce an output. For instance, to identify whether a picture has a mouse or not, all nodes will access the data and deliver an output indicating whether that picture features a mouse.

3) Deep Learning

Similar to neural networks, deep learning also works like a human brain. It powers many machine learning uses like autonomous vehicles, chatbots, and medical diagnostics.

Deep learning networks are neural networks with many layers. The layered network can process enormous data and determine the weight of each link in the network. For instance, in an image recognition system, some layers of the neural network detect individual features of a face, like eyes, nose, or mouth and another layer will tell whether those features appear in a way that indicates a face.

The more layers you have, the more potential you have for doing complex things efficiently. Deep learning demands a great deal of computing power that often questions its economic and environmental sustainability.

How have Machine Learning Algorithms influenced Business Operations Over the years?

Machine learning has become the core feature of some organization’s business models like Netflix’s recommendation algorithm or Google’s search engine. Other companies are also engaging with ML, but they are yet to determine how to use ML to their advantage.

Researchers and industry experts often state that no task will be untouched by machine learning. But, ML cannot take over the job entirely without human intervention. Businesses are already making the most out of machine learning, like:

1) Recommendation Algorithms

The suggestion engines behind Netflix and YouTube or information that appears on your social media feed or product recommendations work based on machine learning technology.

Social media platforms work using machine learning algorithms. Those algorithms try to learn our preferences and show the relevant content to us.

2) Medical Imaging and Diagnostics

One can train machine learning programs to examine medical images or other data and look for specific illnesses. For instance, it can act as a tool that predicts cancer risk based on a mammogram.

3) Self-driving cars

Self-driving cars work based on machine learning technology, especially deep learning in particular.

4) Image Analysis and Object Detection

Machine learning analyzes images for various data, like learning to identify people and tell them apart, even though facial recognition algorithms are controversial.

Businesses can use machine learning for several reasons. For instance, hedge funds use machine learning to analyze the number of cars in parking lots that helps them learn about a company’s performance and make good bets.

5) Fraud Detection

Machine learning algorithms can analyze patterns like where someone generally spends or shops to identify potentially fraudulent credit card transactions, log-in attempts, or spam emails.

6) Chatbots and Voice Assistants

Organizations deploy online chatbots to assist their customers or clients and guide them with their queries and procedures. These algorithms use machine learning and natural language processing. The bots learn from past conversation records to come up with appropriate responses.

Limitations of Machine Learning Algorithms

Though machine learning is a promising technology that opens up new possibilities for businesses, it comes with its own challenges and limitations. Business owners have to be aware of the challenges as well.

1) Explainability

Explainability is nothing but the ability to understand the reason behind machine learning models and how they make decisions. Yes, understanding why a model performs a specific action is always crucial. It is because systems can be fooled or fail in a few tasks which humans can perform well.

Machine learning algorithms can solve most problems, but they can only perform to about 95% of human accuracy. That level of accuracy is not enough for a self-driving vehicle or a program designed to find potential flaws in machinery.

2) Bias and Unintended Outcomes

As humans train machines, human biases can get into algorithms as well. If biased information or data is fed to a machine learning program, it will replicate it too. Chatbots trained on how people converse on Facebook can pick up on offensive language as well.

However, you can overcome bias in machine learning with careful training and putting organizational support behind ethical AI efforts. For example, enabling human-centered AI, the practice of getting input from people of various backgrounds, experiences, and lifestyles while designing AI systems.

Final Thoughts

Machine Learning (ML) is one of the fastest-growing areas in the field of Artificial Intelligence(AI). Machine learning algorithms train computers to do specific tasks without human intervention. Specific areas of machine learning like Neural networks and deep learning replicate the human brain structure and function more intelligently.

Today, ML is the technology behind chatbots, voice assistants, social media feeds, Netflix recommendations, Amazon’s product suggestions, and medical imaging and diagnostics.

Nevertheless, machine learning is dramatically exploding, and it will surely bring an impact in every other industry. We can expect its influence in every niche, from marketing to entertainment and medical to banking. It is already changing the way businesses operate. We can expect that it will continue to enhance business operations in the future.

Though you need not leverage any technology immediately, knowing about it will help you to adapt to it in the future. Thus, business leaders have to understand the principles, potential, and limitations of any upcoming technology.

Hopefully, this blog would have been insightful about several machine learning algorithms, uses, benefits, and limitations. Staying aware of the recent trends and technologies is inevitable for any business owner to stay ahead in business amidst the competition.