Introduction Amidst the fast-changing scenario of the world of artificial intelligence, two new paradigms—Semi-Supervised Learning (SSL) and Reinforcement Learning (RL)—are increasingly taking center stage. Whereas Supervised Learning is based on masses of labeled data, and Unsupervised Learning is based on unlabelled data, semi-supervised learning merges these two, providing a scalable solution with limited labels. Alternatively, reinforcement learning draws inspiration from behavioral psychology—specifically, trial-and-error learning—to learn through rewards and penalties. In this blog, we will discuss what semi-supervised and reinforcement learning are, the way they function, their usage, benefits, disadvantages, and how they are different from one another. If you are attempting to remain competitive in AI and Machine Learning, you must know these advanced learning methods. Want to dive deeper into the foundations of AI? Don’t miss our detailed Machine Learning blog covering supe...