Unsupervised Learning
This subheading focuses on unsupervised learning, where models analyze unlabeled data to discover patterns or clusters. Students delve into algorithms like k-means clustering, hierarchical clustering, and dimensionality reduction techniques like principal component analysis (PCA). They gain insights into the applications of unsupervised learning in areas such as anomaly detection, customer segmentation, and data visualization.
Reinforcement Learning
Reinforcement learning is a dynamic subfield of machine learning that deals with agents interacting with an environment to learn optimal behaviors through trial and error. In this subheading, students explore concepts such as Markov Decision Processes, Q-learning, and policy gradients. They understand how reinforcement learning is used in applications like game playing, robotics, and autonomous systems.
Deep Learning and Neural Networks
The Deep Learning and Neural Networks session focuses on the revolutionary field of deep learning, which has propelled AI to new heights of performance. Students gain a comprehensive understanding of deep learning architectures, such as artificial neural networks with multiple layers. They explore key concepts like activation functions, backpropagation, and gradient descent. Additionally, students gain hands-on experience in building, training, and fine-tuning deep learning models using popular frameworks such as TensorFlow and PyTorch.
