Generative AI Essentials and Applications
Generative AI Essentials and Applications
This course covers all the essentials of Generative AI including fundamental concepts of Artificial Intelligence and Machine Learning, and then progressing on to Neural Networks, Deep Learning, and Large Language Models.
About this course
This course covers all the essentials of Generative AI including fundamental concepts of Artificial Intelligence and Machine Learning, and then progressing on to Neural Networks, Deep Learning, and Large Language Models – culminating into comprehensive expertise in Generative Models, GANs, VAEs, and their applications. In this course, you will gain the knowledge and mathematical understanding required for grasping and working with cutting-edge AI technologies. Successfully completing this course will earn you a certificate.
Course outline
Fundamentals and Limitations of Artificial Intelligence
In this module, we'll trace AI's evolution, distinguish narrow from general AI, and examine its role in NLP, ethics, data dependency, and the need for human oversight.
Basics of Machine Learning and Core Algorithms
In this module, we'll understand supervised, unsupervised, and reinforcement learning, then survey the classification and regression algorithms that put these concepts into practice.
Applications and Challenges in Machine Learning
In this module, we'll discover how ML powers forecasting and autonomous vehicles, and identify the key technical and practical challenges that arise during deployment.
Introduction to Neural networks
In this module, we'll learn how neural networks are structured, explore their major types, and see where they are applied across real-world problems.
Deep Learning, CNN and RNN Concepts
In this module, we'll grasp deep learning's core principles, advantages, and limitations, then dive into how CNNs handle visual data and RNNs process sequences.
Large Language Models and Their Applications
In this module, we'll study LLM architecture and Transformer design, then explore how these models drive translation, conversational AI, and chatbot solutions.
Generative AI Concepts and Models
In this module, we'll contrast generative and discriminative models, then examine how GANs and VAEs work and where they are applied.
Mathematical Foundations of Generative AI
In this module, we'll build fluency in the probability, statistics, and sampling methods that underpin how generative AI models are designed and trained.
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