Introduction to Artificial Intelligence
A foundational course covering the core concepts, history, and applications of AI, including search algorithms, knowledge representation, and expert systems.
Machine Learning Fundamentals
Understand the principles of supervised, unsupervised, and reinforcement learning. Explore algorithms like linear regression, decision trees, clustering, and model evaluation.
Deep Learning Specialization
Master neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers for complex tasks like image recognition and natural language processing.
Natural Language Processing (NLP) Essentials
Learn to build systems that understand, interpret, and generate human language. Covers text preprocessing, sentiment analysis, chatbots, and language modeling.
Computer Vision Applications
Explore techniques for enabling computers to "see" and interpret visual information. Covers image processing, object detection, facial recognition, and augmented reality.
MLOps: Productionizing ML Models
Learn the practices and tools for deploying, monitoring, and maintaining machine learning models in production environments, ensuring scalability and reliability.