1 -Intro Domain 1 - Fundamentals of Machine Learning and Artificial Intelligence
2 -Basic AI terms (AI, ML, deep learning, neural networks, computer vision, etc) P1
3 -Basic AI terms (AI, ML, deep learning, neural networks, computer vision, etc) P2
4 -Similarities and differences between AI, ML, and deep learning
5 -Inferences, Data and Learning Techniques in AI. PARTE 1
6 -Inferences, Data and Learning Techniques in AI. PARTE 2
7 -Inferences, Data and Learning Techniques in AI. PARTE 3
8 -Recognizing the applications where AIML can add value
9 -Determining when AIML solutions are not appropriate
10 -Selecting the appropriate ML techniques for specific use cases
11 -Practical AI use cases - AWS managed AIML services. PART 1
12 -Practical AI use cases - AWS managed AIML services. PART 2
13 -Examples of real-world AI applications
14 -Machine Learning Development Lifecycle. PART 1 (ML pipeline, lifecycle, etc)
15 -Machine Learning Development Lifecycle. PART 2 (data collection, data pre, etc)
16 -Machine Learning Development Lifecycle. PART 3 (model training, tuning)
17 -Machine Learning Development Lifecycle. PART 4 (Evaluation)
18 -Machine Learning Development Lifecycle. PART 5 (Evaluation)
19 -Machine Learning Development Lifecycle. PART 6 (Deployment)
20 -Machine Learning Development Lifecycle. PART 7 (Monitoring)
21 -Fundamental concepts of ML operations (MLOps)
