Machine learning

Knowing the tools that spur innovation is now essential in this age of the digital revolution, not a luxury. Machine learning is one technology that has been leading the charge in this revolution. The goal of this post is to demystify machine learning by offering a thorough how-to for both novices and experts.

What is machine learning?

Machine learning, or ML for short, is a branch of artificial intelligence (AI) that focuses on creating computer algorithms that learn automatically from data and experience. Put more simply, machine learning allows computers to learn from data and develop predictions or judgments without the need for explicit programming.

Fundamentally, machine learning is all about developing and applying algorithms that help with these choices and forecasts. As they handle more data, these algorithms are built to perform better over time, becoming more precise and efficient.

In classical programming, a computer carries out a task by following a predetermined set of instructions. In machine learning, on the other hand, the computer is given a task to do along with a set of examples (data), and its job is to determine how to complete the task based on the examples provided.

For example, we don't provide a computer with explicit instructions on what a cat looks like if we want it to recognize photographs of cats. Rather, we feed the machine learning system thousands of cat photos, allowing it to identify the typical traits and patterns that characterize a cat. The program learns to identify cats over time by processing an increasing number of photos, even ones it has never seen before.

Machine learning is very strong and flexible because of its capacity to learn from data and get better over time. Numerous modern technology innovations, such as voice assistants, recommendation engines, self-driving cars, and predictive analytics, are propelled by this principle.