🤖 AI Masterclass

Course content

• Section
  1

  Section 1 – AI Foundations & Fundamentals

  • 50 live classes
  • 01:29:35
  • 1.1 – What Artificial Intelligence Really Is (and Isn’t) 00:10:06
  • 1.2 – History of AI: From Logic Machines to Neural Networks 00:04:31
  • 1.3 – Core AI Categories: Narrow, General, and Superintelligence 00:08:36
  • 1.4 – The Difference Between AI, Machine Learning, and Deep Learning 00:05:04
  • 1.5 – Key AI Terms Every Beginner Must Know 00:03:19
  • 1.6 – Understanding Data: The Fuel for AI 00:05:48
  • 1.7 – How AI Learns: Algorithms and Models Explained Simply 00:05:28
  • 1.8 – Why Big Data Matters for AI Accuracy 00:07:00
  • 1.9 – Introduction to Neural Networks 00:05:17
  • 1.10 – Activation Functions: The Brain Signals of AI 00:04:44
  • 1.11 – What Makes AI “Smart”? Pattern Recognition Basics 00:04:11
  • 1.12 – The Role of Mathematics in AI 00:07:50
  • 1.13 – Understanding Linear Algebra for AI Applications 00:04:10
  • 1.14 – Probability & Statistics in AI Predictions 00:07:05
  • 1.15 – How AI Uses Logic to Make Decisions 00:06:26
  • 1.16 – The Training Process: Feeding Data to AI Models 00:00:00
  • 1.17 – Bias in AI: How It Happens and Why It’s Dangerous 00:00:00
  • 1.18 – Common AI Myths Debunked 00:00:00
  • 1.19 – AI vs. Human Intelligence: A Comparison 00:00:00
  • 1.20 – The Role of Cloud Computing in AI 00:00:00
  • 1.21 – Understanding AI Pipelines and Workflows 00:00:00
  • 1.22 – How AI Models Are Stored and Retrieved 00:00:00
  • 1.23 – The Importance of Clean Data 00:00:00
  • 1.24 – What is Overfitting and Underfitting? 00:00:00
  • 1.25 – AI’s Relationship with the Internet of Things (IoT) 00:00:00
  • 1.26 – Open-Source AI vs. Proprietary AI Systems 00:00:00
  • 1.27 – Understanding AI APIs and How They Work 00:00:00
  • 1.28 – How AI Processes Natural Language 00:00:00
  • 1.29 – Machine Vision: How AI Sees Images 00:00:00
  • 1.30 – Reinforcement Learning Basics 00:00:00
  • 1.31 – AI in Everyday Life: Hidden Uses Around You 00:00:00
  • 1.32 – AI’s Role in Predictive Analytics 00:00:00
  • 1.33 – Introduction to AI Ethics and Safety 00:00:00
  • 1.34 – AI’s Impact on Different Industries 00:00:00
  • 1.35 – Common AI Tools and Platforms for Beginners 00:00:00
  • 1.36 – The Cost of Building AI Models 00:00:00
  • 1.37 – How AI Models Are Updated and Improved 00:00:00
  • 1.38 – Edge AI: Running AI on Small Devices 00:00:00
  • 1.39 – Understanding AI Benchmarks and Performance 00:00:00
  • 1.40 – AI Model Deployment in Real-World Systems 00:00:00
  • 1.41 – The Role of Data Annotation in AI 00:00:00
  • 1.42 – Understanding AI Scalability 00:00:00
  • 1.43 – AI for Accessibility and Inclusion 00:00:00
  • 1.44 – Understanding Tokenization in Language Models 00:00:00
  • 1.45 – Transfer Learning Basics 00:00:00
  • 1.46 – AI Research Papers: How to Read and Understand Them 00:00:00
  • 1.47 – Current AI Limitations and Challenges 00:00:00
  • 1.48 – The Future of AI: Trends and Predictions 00:00:00
  • 1.49 – How to Choose Your AI Learning Path 00:00:00
  • 1.50 – Building Your First Simple AI Model 00:00:00