- Cognitive Computing
This document explores the concept, elements, and real-world impact of cognitive computing. It covers how cognitive systems mimic human thought their benefits, and applications across industries such as healthcare finance, and customer service.
- AI Terminologies
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.
- Machine Learning
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.
- Machine Learning Techniques and Training
Overview of machine learning techniques, including supervised, unsupervised and reinforcement learning, with a focus on regression, classification, neural networks, and the training process.
- Deep Learning
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.
- Neural Networks
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.
- Machine Learning vs Deep Learning
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.
- Generative AI Models
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.
- Large Language Models
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.
- Foundation Models
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.
- Activity
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.
- NLP, Speech, and Vision
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.
- What is NLP
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.
- Self-Driving Cars
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.
- AI, Cloud, Edge, and IoT
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.
- Module Summary
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.