AI/ML Introduction: Episode #5: What is Machine Learning?

Aruna Pattam
4 min readDec 13, 2022


What is Machine Learning?

In recent years, machine learning has become one of the most talked-about topics in business and technology.

But what exactly is machine learning?

In this blog post, I will explore what machine learning is, how it works, and some of its potential applications.

After reading this post, you’ll have a better understanding of this cutting-edge technology and how it could transform your business.

Machine Learning — Definition

Machine learning has been an area of staggering progress in recent years.

Put simply, machine learning is the process through which computers can learn to solve complex tasks without being explicitly programmed — it requires algorithms to iteratively update themselves based on data.

In other words, machine learning algorithms are designed to improve with experience, as they acquire knowledge from datasets, making them incredibly powerful tools for data analysis and AI applications.

With machine learning becoming increasingly sophisticated, it holds huge promise for helping to solve many of today’s intractable problems in a range of different industries.

Types of Machine Learning algorithms

Machine learning algorithms are a fascinating area of computer science, allowing us to use advanced algorithms when faced with complex and extensive data sets.

Machine learning can be divided into 3 main categories: supervised, unsupervised and reinforcement.

Supervised learning algorithms require human input to generate labels for the data before any parameters are established, while unsupervised learning algorithms uncover patterns without guidance from human input. Finally, reinforcement learning works to maximise a reward or goal by changing the behaviour of the algorithm given feedback from its environment.

Each type of machine learning algorithm has its advantages and disadvantages; however, harnessing their immense potential can bring about incredible results. The possibilities within this area continue to develop every day as developers find more way to use these powerful tools.

Machine Learning Applications

Machine learning is a field of Artificial Intelligence that uses algorithms and other data-driven techniques to automate decision making. This has made it extremely useful in multiple industries, such as healthcare, finance, automotive and manufacturing, among many others.

In Healthcare

Machine learning helps in predictive diagnosis to accurately predict a patient’s outcome based on past data. Furthermore, machine learning can assist in identifying the most effective medical treatments and helping alert clinicians to potential health risks.

For example, by using supervised learning algorithms, doctors can use historical data to predict a patient’s future health. This helps save time and money while improving healthcare outcomes

In Finance

Machine learning can be used to detect fraudulent transactions, predict stock prices and automate trading decisions.

For example, supervised learning algorithms can help identify suspicious activity by examining a customer’s spending habits or financial history.

Furthermore, using unsupervised learning algorithms, systems can be trained to automatically detect patterns in large data sets and make predictions about future events.

In Manufacturing

Machine learning can be used to automate and improve production processes.

By using supervised learning algorithms, manufacturers can detect anomalies in their assembly lines and other crucial machines

In addition, reinforcement learning algorithms can help reduce maintenance costs by predicting when a machine will require service or repair.

In Retail

Machine learning can be used to improve customer service in a number of ways.

For example, unsupervised learning algorithms can be used to better understand customers’ behaviour and preferences so that retailers can personalise their offers accordingly.

In addition, supervised learning algorithms can help automate customer support tasks such as suggesting products or services based on the customer’s past purchases.

In Telco

Machine learning can help streamline telecom and mobile networks.

Supervised learning algorithms can be used to detect anomalies in data traffic and alert technicians of potential issues before they become major problems.

In addition, reinforcement learning can help automate the process of optimising network resources by predicting when and where additional resources are needed.

These are just a few of the many applications of machine learning in today’s world.

With its potential to revolutionise numerous industries, it is no wonder that this area continues to be an exciting field for innovation and development

How to get started with Machine Learning

For those interested in getting started with machine learning, there are a few things you should know before diving into this cutting-edge technology.

Firstly, a good foundation in computer science and mathematics is needed to truly understand the fundamentals;

Secondly, familiarise yourself with some of the popular programming language such as Python and Java which will enable you to get up and running quickly;

Thirdly, it’s beneficial to discover some of the most powerful machine learning tools such as TensorFlow, Scikit-Learn and Keras and

Fourth, build your own machine learning project to gain practical experience and

Finally, to put your knowledge into practice, it’s important to get involved in online communities such as Kaggle or Stack Overflow where you can both learn from experts and collaborate with other machine learning enthusiasts.

By following these steps, you can make sure that you have the necessary tools and knowledge to take your machine learning skills to the next level. With enough practice and dedication, you can start developing projects that have real-world applications and make an impact in any industry.


Machine learning is a powerful tool that can be used in a variety of industries to achieve different goals.

While there are many different types of machine learning algorithms, they all have the same goal: to help machines learn from data so that they can improve their performance on tasks.

Some examples of machine learning in action include spam filters, recommendations systems, and self-driving cars.

If you’re interested in getting started with machine learning, consider taking an online course or reading one of the many excellent introductory books on the subject.

With so much potential, it’s worth taking the time to learn about this exciting field.