AI & ML & DL Concepts

So we've learned about quite concrete AWS services, but in this section we're going to take a step back and learn about AI and machine learning overall.

This section is a little bit more theory-oriented and it has a lot of information. Don't worry and don't go too much into the details.

What I want you to understand, is the general idea behind AI, machine learning, deep learning, and generative AI.

If you've understood this, then you will be acing your questions at the exam.

I hope you will really understand the behind those scenes of AI and machine learning.

Index:

1. AI,ML, Deep Learning and GenAI
2. ML Terms You May Encounter in the Exam
3. Training Data
4. Supervised Learning
5. Unsupervised Learning
6. Self-Supervised Learning
7. Reinforcement Learning
8. RLHF - Reinforcement Learning from Human Feedback
9. Model Fit, Bias, and Variance
10. Model Evaluation Metrics
11. Machine Learning - Inferencing
12. Phases of a Machine Learning Project
13. Hyperparameters
14. When is ML not appropriate?
15. Quiz 5