Deep Learning is a subset of ML focused on training neural networks with  multiple layers to automatically learn hierarchical representations of  data. It powers advanced systems for tasks like image recognition,  natural language processing, and reinforcement learning. Deep Learning  has transformed industries, enabling breakthroughs in computer vision,  healthcare, and more, with ongoing research aimed at improving  interpretability and efficiency.

Supervised Learning is a core ML technique where the algorithm learns from labeled data, mapping inputs to corresponding outputs. It's used for tasks like classification and regression, predicting labels or values based on input features. Supervised Learning finds applications in image recognition, spam detection, sentiment analysis, and more, driving innovation across industries.

Unsupervised Learning is a technique where the algorithm learns from unlabeled data, finding patterns and structures without explicit guidance. It's used for tasks like clustering and dimensionality reduction to uncover hidden insights in data. Unsupervised Learning is vital for anomaly detection, data compression, and exploratory analysis, driving discoveries across domains.