Machine Learning Basics Online Course

Machine Learning Basics Online Course

This beginner-friendly course introduces the fundamentals of Machine Learning, starting with the evolution of Artificial Intelligence and the differences between traditional programming and modern ML approaches. It explains key concepts such as supervised, unsupervised, and reinforcement learning using simple examples of regression, clustering, and dimensionality reduction. The course also covers how ML models are trained for real-world use and explores generative AI, including neural networks and large language models. By the end, learners gain a clear understanding of Machine Learning, its challenges, and its role in today’s rapidly evolving AI landscape.

Who should you take this course?

This course is ideal for beginners with no prior experience in Machine Learning or Artificial Intelligence, students and fresh graduates looking to build a strong foundation in AI concepts, working professionals who want to understand how Machine Learning is applied in real-world scenarios, and anyone curious about emerging technologies such as generative AI and large language models.

What you will learn

• Understand the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning.
• Identify and differentiate major types of Machine Learning systems.
• Learn how Machine Learning models are trained and generalized for real-world use.
• Explore neural networks and their practical applications.
• Recognize common AI challenges, including bias and prompt sensitivity.
• Apply Machine Learning concepts effectively to real-world AI use cases.

Course Outline

Getting Started with Level 1!

• Welcome!

The Rise of Artificial Intelligence

• Artificial Intelligence (AI) - The Future
• Artificial Intelligence
• Classical Programming
• Machine Learning
• Deep Learning
• Applied vs. Generalized Artificial Intelligence (AI)
• Why Do We Need AI Today?

Introduction to Machine Learning

• Machine Learning (ML) Terminology
• The Black Box Metaphor
• Features and Labels
• Training a Model
• Aiming for Generalization

Classification of Machine Learning (ML) Systems

• The Degree of Supervision
• Supervised Learning
• Classification
• Regression
• Unsupervised Learning
• Clustering
• Dimension Reduction
• Reinforcement Learning
• Decision-Making Agent

The Magic Behind Generative AI

• Introduction
• Artificial Neural Networks
• Deep Learning Architectures
• Foundation Models
• Large Language Models (LLMs)
• Model Types
• Prompt and Tokens
• Total Tokens and Context Window
• Next Token Please!
• Self-Supervised Learning
• Improving and Adapting LLMs
• Summary

Key Challenges and Limitations

• Introduction
• Prompt Sensitivity
• Knowledge Cutoff
• It is not Deterministic
• Structured Data
• Hallucinations
• Lack of Common Sense
• Bias and Fairness
• Data Privacy, Security, and Misuse
• Summary

Unleash the Power of Generative AI

• Introduction
• Text-Image-Video-Audio Generation
• Web-Based vs Application-Based (APIs)
• Use Case - Brainstorm Assistant
• Use Case - Summarization
• Use Case – Text Enhancement
• Use Case - Code Generation
• Use Case – Content as a Framework
• Use Case – Images on Demand
• Use Case – Boosting AI-Based Apps
• Best Practices for Prompts
• Summary

Course Summary

• Let's Recap and Thank You!