Generative AI with Machine Learning and LLMs
Generative AI with Machine Learning and LLMs
Learn AI, ML types, neural networks, deep learning, LLMs, Transformers, GANs, VAEs, and GenAI math. Enroll in this free generative AI and machine learning course to build a strong base in generative models.
About this course
This Introduction to Generative AI and LLMs course equips you with the essential skills to understand how AI, machine learning, deep learning, and Generative AI work together. You’ll learn the evolution of AI, the difference between narrow and general AI, supervised, unsupervised, and reinforcement learning, and how classification and regression algorithms support real-world use cases. The course also covers neural networks, CNNs, RNNs, Large Language Models, Transformer design, GANs, VAEs, and the mathematical foundations behind generative models. You’ll also gain the ability to assess AI use cases, understand model limitations, and build a strong foundation for advanced learning in Generative AI, machine learning, and AI-driven applications. By the end of this Generative AI with Machine Learning free course, you’ll be able to explain core AI and ML concepts, understand how LLMs power translation, conversational AI, and chatbot solutions, and recognize how generative models create new outputs.
Course outline
Aritificial Intelligence and its Applications
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.
Machine Learning and its Types
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.
Different Neural networks Types and its Applications
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.
Basics of Generative AI
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.
Get access to the complete curriculum once you enroll in the course
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