Overview

This free Generative AI course is designed to provide you with a comprehensive understanding of key Artificial Intelligence concepts. You’ll start by learning the fundamentals of AI, addressing its constraints and challenges. The course will cover essential Machine Learning algorithms and their practical applications. You’ll gain insight into Neural Networks and explore Deep Learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). As we progress, you will also get an Introduction to AI Tools/Platforms, covering popular tools such as TensorFlow, PyTorch, and Hugging Face. This will help you gain a basic understanding of the tools commonly used in AI.The course will also emphasize the significance of Large Language Models (LLMs) and demonstrate how they are used across various industries. You’ll explore Generative Models in AI, understanding both their mathematical underpinnings and real-world applications. This free Generative AI course with a certificate offers a deep dive into the field, equipping you with the knowledge and skills to apply this cutting-edge technology effectively. By the end of the course, you will have a strong foundation in Generative AI, ready to tackle real-world challenges.

Syllabus

Fundamentals of Artificial Intelligence

In this module, you will learn about the evolution of AI, from its historical milestones to its classification into Narrow AI and General AI and gain insights into AI's pivotal role in Natural Language Processing and its ethical implications.

Limitations and Challenges of AI

In this module, you will explore the limitations and challenges of AI, including its reliance on data quality and the crucial requirement for human intervention and judgment in the implementation and governance of AI systems."

Basics - Machine Learning

In this module, you will delve into the fundamental concepts of machine learning, including exploring various types of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning."

Machine Learning Algorithms

In this module, you will get a concise overview of machine learning algorithms, including classification algorithms, regression algorithms, and more.

Applications and Challenges in Machine Learning

In this module, you will explore the diverse applications of machine learning, including forecasting and self-driving cars. Additionally, you will also look into the challenges associated with ML algorithms.

Introduction - Neural Networks

In this module, you will be introduced to Neural Networks. You will also explore the various types of Neural Networks and their practical applications.

Brief of Deep Learning, CNN, and RNN Concepts

In this module, you will get an overview of the concept of deep learning, discussing its advantages and challenges. You will also learn the concepts of CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks).

Deep Learning Overview

In this module, you will explore the fundamental principles of deep learning, delve into their applications, examine their key characteristics, and discuss their significance.

Large Language Models

In this module, you will explore the concept of Large Language Models, their evolutionary development, and architectural components, with a specific focus on Transformer models.

Generative AI Concepts

This module will introduce you to Generative AI, exploring its key characteristics and the training process. Additionally, you will delve into a detailed comparison between Generative and Discriminative Models.

Generative Models in AI

In this module, you will delve into the diverse realm of Generative AI, exploring key techniques like GANs and VAEs, as well as their practical applications.

Mathematical Foundations of Generative AI

In this module, you will explore the significance of Probability and Statistics in Generative AI and different sampling methods and machine learning algorithms that can be applied in Generative AI.

Ethics and Responsible AI

Ethics and Responsible AI: This module addresses the fundamental principles of AI ethics, importance of fairness, accountability, and transparency in the development of AI technologies.