AI/ML

Before diving into machine learning, it’s important to get familiar with the basic concepts and key terms often used in this field. As you go deeper into the subject, one of the biggest challenges will be remembering what certain terms mean. Without a solid understanding of these basics, learning advanced topics can feel much harder than it needs to be.

This introduction covers the essential high-level concepts you should know — many of which are concepts from high school / engineering days. Taking a moment to revisit them now will make your learning journey much smoother later on.

Machine learning (ML) is a branch of artificial intelligence (AI) that empowers systems to learn from data and make predictions or decisions without explicit programming. ML algorithms analyze data patterns to generate informed predictions or classifications.

ML is broadly categorized into Supervised learning, Unsupervised learning, and Reinforcement learning. Common applications include recommendation systems, predictive analytics, fraud detection, speech recognition, sentiment analysis, machine translation, self-driving cars, and healthcare diagnostics. ML plays a crucial role in automating tasks and extracting insights from massive datasets.