AI Concepts for Tech Professionals (Video Course)

Introduction
1 AI Concepts for Tech Professionals Introduction

Lesson 1 Basic AI Concepts
2 Learning objectives
3 Basic AI Terminology
4 Introduction to Machine Learning
5 Introduction to Deep Learning
6 Question Breakdown 1
7 Question Breakdown 2

Lesson 2 Practical Use Cases For AI
8 Learning objectives
9 AI Patterns and Anti-patterns
10 ML Techniques
11 Real-world AI Applications
12 AWS Managed AIML Services
13 Question Breakdown 1
14 Question Breakdown 2

Lesson 3 Foundation Model Design
15 Learning objectives
16 Pre-trained Model Selection Criteria
17 Model Inference Parameters
18 Introduction to RAG
19 Introduction to Vector Databases
20 AWS Vector Database Service
21 Foundation Model Customization Cost Tradeoffs
22 Generative AI Agents
23 Question Breakdown 1
24 Question Breakdown 2

Lesson 4 Foundation Model Training, Performance, and Fine Tuning
25 Learning objectives
26 Foundation Model Training
27 Foundation Model Performance Metrics and Evaluation
28 Foundation Model Business Objective Criteria
29 Foundation Model Fine-tuning
30 Foundation Model Data Preparation
31 Question Breakdown 1
32 Question Breakdown 2

Lesson 5 Basic Concepts of Generative AI
33 Learning objectives
34 Basic Generative AI Terminology
35 Generative AI Use Cases
36 Foundation Model Lifecycle
37 Question Breakdown 1
38 Question Breakdown 2

Lesson 6 Generative AI Capabilities and Limitations
39 Learning objectives
40 Generative AI Advantages
41 Generative AI Disadvantages
42 Model Selection Decision Tree
43 Generative AI Business Value and Metrics
44 Question Breakdown 1
45 Question Breakdown 2

Lesson 7 Prompt Engineering
46 Learning objectives
47 Prompt Workflow
48 Prompt Engineering Concepts
49 Prompt Engineering Techniques
50 Prompt Engineering Best Practices
51 Prompt Engineering Risks and Limitations
52 Question Breakdown 1
53 Question Breakdown 2

Lesson 8 ML Development Lifecycle
54 Learning objectives
55 ML Pipeline Components
56 ML Model Sources and Deployment Types
57 Introduction to ML Ops
58 AWS ML Pipeline Services
59 ML Model Performance Metrics
60 Question Breakdown 1
61 Question Breakdown 2

Lesson 9 Responsible AI System Development
62 Learning objectives
63 Responsible AI Features
64 AWS Responsible AI Tools
65 Responsible AI Model Selection Practices
66 Generative AI Legal Risks
67 AI Dataset Characteristics
68 AI Bias and Variance
69 AWS AI Bias Detection Tools
70 Question Breakdown 1
71 Question Breakdown 2

Lesson 10 Transparent and Explainable AI Models
72 Learning objectives
73 Transparency and Explainability Definitions
74 AWS Transparency and Explainability Tools
75 AI Model Safety and Transparency Tradeoffs
76 Human-centered AI Design Principles
77 Question Breakdown 1
78 Question Breakdown 2

Lesson 11 AI Security
79 Learning objectives
80 AWS AI Security Services and Features
81 Data Citations and Origin Documentation
82 Secure Data Engineering Best Practices
83 AI Security and Privacy Considerations
84 Question Breakdown 1
85 Question Breakdown 2

Lesson 12 AI Governance and Compliance
86 Learning objectives
87 AWS Governance and Compliance Services
88 Data Governance Strategies
89 Governance Protocols and Compliance Standards
90 Question Breakdown 1
91 Question Breakdown 2

Module 1 Introduction to Artificial Intelligence
92 Module Introduction

Module 2 Foundation Models
93 Module Introduction

Module 3 Generative AI
94 Module Introduction

Module 4 AIML Workload Development
95 Module Introduction

Module 5 AI Safety and Security
96 Module Introduction

Summary
97 AI Concepts for Tech Professionals Summary