AI (Artificial Intelligence) and Machine Learning are revolutionizing the way we interact with technology. AI refers to the simulation of human intelligence processes by machines, whereas Machine Learning is a subset of AI that allows machines to learn from data without being explicitly programmed. In this course, we will delve into the fundamentals of AI and Machine Learning, exploring how these technologies are shaping the future of various industries.

Throughout this bootcamp, you will learn about the different types of AI, such as Narrow AI and General AI, and the various applications of Machine Learning, including image recognition, natural language processing, and predictive analytics. You will also gain hands-on experience with popular Machine Learning algorithms, like linear regression, decision trees, and neural networks. By the end of this course, you will have a solid foundation in AI and Machine Learning, enabling you to leverage these technologies in your own projects and career.

Whether you are a beginner looking to understand the basics of AI and Machine Learning or a seasoned professional hoping to expand your knowledge, this bootcamp will provide you with the tools and insights you need to succeed in the rapidly evolving field of artificial intelligence. By equipping yourself with the skills to create intelligent machines and systems, you will be prepared to tackle the challenges and opportunities that lie ahead in the world of AI and Machine Learning.

Key Lesson Concepts:

• AI and Machine Learning revolutionize technology
• Difference between AI and Machine Learning
• Types of AI and applications of Machine Learning
• Hands-on experience with Machine Learning algorithms
• Tools and insights for success in AI and Machine Learning

Learning Objectives

• Define AI vs. Machine Learning
• Differentiate Narrow AI, General AI, and key ML applications
• Identify real‑world use cases

Lesson Content
Artificial Intelligence (AI) simulates human intelligence in machines; Machine Learning (ML) is a subset that enables systems to learn from data. Narrow AI performs specific tasks (e.g., recommendation engines), while General AI would match human cognitive abilities (still theoretical). ML powers image recognition (e.g., facial tagging), natural language processing (e.g., chatbots), and predictive analytics (e.g., sales forecasting).

Popular algorithms include linear regression (predict continuous outcomes), decision trees (rule‑based classification), and neural networks (deep learning for complex pattern recognition). Industries leveraging AI/ML span healthcare (diagnosis), finance (fraud detection), retail (customer personalization), and beyond.