Machine Learning: Not Just for Robots Anymore

When you hear "machine learning," you might envision robots performing complex tasks in a factory or laboratory. However, machine learning is not just for robots anymore. It's a growing field with practical applications in everything from healthcare to finance. And this one is to get you started.


Tech Brew Hub

1 min read

Machine Learning and Artificial intelligence are two of the most exciting and rapidly-evolving fields in technology today. But what are they exactly, and how do they vary?

The purpose of Artificial Intelligence (AI), a branch of computer science, is to build machines capable of doing jobs that traditionally necessitate human intellect, such as speech recognition, visual perception, decision-making, and language understanding.

Conversely, ML is a subset of AI that involves training a computer to learn from data without being explicitly programmed. ML aims to create models or algorithms to make predictions or decisions based on data.

One of the most common types of ML is supervised learning, in which a model is trained on a labeled dataset, where the correct output is provided for each input. This type of learning is used in a wide range of applications, from image and speech recognition to natural language processing.

Another essential type of Machine Learning is unsupervised learning, which is used when the correct output is not provided for each input. Instead, the model must find patterns or structures in the data independently. Unsupervised learning is often used in applications such as anomaly detection and clustering.

Reinforcement learning is another type of ML that involves training an agent to make decisions by interacting with its environment and receiving feedback as rewards or penalties. This learning type is used in gaming and robotics applications.

To start with AI and ML, you'll need a basic understanding of programming and statistics and knowledge of the programming languages and libraries commonly used in the field, such as Python, R, and TensorFlow. And there are many resources available online to help you learn, including tutorials, courses, and books.

So don't hesitate to contact us if you're interested in learning more about AI and ML. And dive right in to start experimenting with your projects. Who knows? You might be the next AI or ML expert!