Docs

This module explores the evolution from traditional AI to generative AI and foundation models, explaining modes of AI consumption, the importance of open knowledge, and the impact of value creation and extraction in the AI economy.

This module explores how AI is transforming business operations, automating workflows, enhancing decision-making, and driving efficiency and innovation across industries such as customer service, HR, accounting, marketing manufacturing, and healthcare.

This module introduces robotics and automation, explaining how robots work how AI technologies are integrated, and how robotics enables automation across industries, including the role of cobots and robotic process automation.

This module explores the shift from monolithic models to compound AI systems highlighting how integrating models with tools and databases enables more flexible, accurate, and adaptable solutions for real-world tasks.

This page provides an interactive recap of the key concepts covered in the "Introduction to AI" module. Use these activities to test your understanding and see how the core ideas connect.

This document provides an overview of large language models (LLMs), their foundation model origins, generative capabilities, and business applications. It explains how LLMs are trained, their advantages, and the role of prompting and tuning in real-world use cases.

This document introduces generative AI models, their types, and applications. It explains how these models use machine learning and deep learning to create new content, and highlights the differences between unimodal and multimodal models.

This document explains the key differences between machine learning and deep learning, using practical analogies and examples. It covers their relationship, data requirements, feature engineering, and the role of neural networks in deep learning.

This document introduces neural networks, their structure, types, and training process. It explains how neural networks are inspired by the human brain and highlights their applications in pattern recognition, image analysis, and sequential data processing.

This document provides an overview of deep learning, its key concepts applications in various fields, and the different types of models used. It also covers the training process and recent advancements in the field.

Explains what AI agents are, their characteristics, types, and real-world applications, including multi-agent systems and their impact on automation and decision-making.

Explains the basics of IoT, cloud computing, and edge computing, and how their convergence with AI enables smart, real-time applications for a connected future.

Explains the relationship between AI, machine learning, deep learning foundation models, generative AI, and large language models. Covers definitions, distinctions, and the evolution of foundational AI technologies.

Summarizes key concepts in machine learning, deep learning, generative AI cognitive computing, NLP, computer vision, IoT, cloud, and edge computing with real-world applications and model architectures.

Explains natural language processing (NLP), how it translates unstructured text into structured data, and the key steps and tools in the NLP pipeline with real-world use cases and examples.

Explores natural language processing (NLP), speech technologies, and computer vision, including their definitions, applications, and how neural networks enable machines to process language and visual data.

Explores the technology, challenges, and societal impact of self-driving cars including 3D object detection, sensor fusion, and the role of computer vision in autonomous vehicles.

Overview of machine learning techniques, including supervised, unsupervised and reinforcement learning, with a focus on regression, classification, neural networks, and the training process.

This document introduces the fundamentals of machine learning, its types, and how ML models differ from traditional algorithms. It explains supervised unsupervised, and reinforcement learning with real-world examples and use cases.

This document introduces key terms and concepts in artificial intelligence including machine learning, deep learning, and neural networks. It explains AI categories and highlights how these technologies enable intelligent systems and real-world applications.