Glossary - Machine Learning
Hi, I am Japkeerat. I am working as a Machine Learning Engineer since January 2020, straight out of college. During this period, I've worked on extremely challenging projects - Security Vulnerability Detection using Graph Neural Networks, User Segmentation for better click through rate of notifications, and MLOps Infrastructure development for startups, to name a few. I keep my articles precise, maximum of 4 minutes of reading time. I'm currently actively writing 2 series - one for beginners in Machine Learning and another related to more advance concepts. The newsletter, if you subscribe to, will send 1 article every Thursday on the advance concepts.
Machine Learning - Machine learning is a way for computers to learn from data and make decisions or predictions without being explicitly programmed for each task. Instead of following fixed rules, the computer analyzes patterns in the data to improve its performance over time. Think of it like teaching a child by showing them examples, and they learn to recognize or predict things on their own.
Supervised Machine Learning - Supervised machine learning is a type of machine learning where a computer learns from labeled data. This means that the data used for training includes both the input (features) and the correct output (labels). The computer analyzes these examples to learn the relationship between inputs and outputs so it can make predictions on new, unseen data. It's like teaching a student with answer keys: when they study the examples with the correct answers, they learn how to solve similar problems in the future.
Unsupervised Machine Learning - Unsupervised machine learning is a type of machine learning where a computer learns from data that doesn't have labeled answers. Instead of being given the correct output, the computer looks for patterns and relationships in the data on its own. It’s like exploring a new city without a map—you're trying to find groups of similar places or discover hidden patterns without anyone telling you what to look for. Common tasks include clustering similar items together or reducing the dimensions of data to find simpler representations.
