For a coder, Programming languages serve as the bridge between humans and computers. Languages like Python, C, Ruby, Java, HTML, React, and more enable us to give instructions to computers.
Now, imagine if we had a smart assistant - that's where Machine Learning (ML) comes in. ML is like a digital helper that can tackle the tricky parts of coding for us.
When we face a problem that involves lots of complex math every time we run a calculation, we let ML do the heavy lifting. It learns from data and figures out how to handle these calculations on its own. This means we can focus on the creative and problem-solving aspects of coding, making our work easier and more efficient.
In a nutshell, "Machine Learning" is like having a coding partner that handles the math-heavy tasks, freeing us up to do what we do best: creating and solving problems with code.
Machine Learning (ML) works by allowing computer systems to learn and make decisions or predictions from data without being explicitly programmed with specific instructions for every possible scenario.
Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning from data, without being explicitly programmed.
In simpler terms, it's a way for computers to learn from examples and data to make predictions or decisions without having specific instructions.
Traditional programming involves writing explicit instructions to perform specific tasks. For example, if you were building a spam filter, traditional programming might involve writing rules like "if an email contains the word 'discount' and 'money-back guarantee,' mark it as spam."
In contrast, in machine learning, you don't provide these explicit rules. Instead, the algorithm learns these rules automatically from the data. For the spam filter, you'd give the algorithm a dataset of emails, some labeled as spam and some as not spam. The ML algorithm would analyze the text, identify patterns, and learn to distinguish spam from non-spam emails without you specifying the exact rules.
In essence, "without being explicitly programmed" means that the machine learning model figures out the rules and patterns on its own by learning from data, which is one of the key advantages of ML—it can handle complex and data-driven tasks that are difficult to program manually.