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OReilly Succeeding with AI video edition

Video Introducing this tutorial

1 - Chapter 1. Introduction
2 - Chapter 1. AI and the Age of Implementation
3 - Chapter 1. Machine learning from 10,000 feet
4 - Chapter 1. Start by understanding the possible business actions
5 - Chapter 1. AI finds correlations, not causes!
6 - Chapter 1. What is CLUE
7 - Chapter 1. Exercises
8 - Chapter 2. How to use AI in your business
9 - Chapter 2. How is AI used
10 - Chapter 2. Making money with AI
11 - Chapter 2. Finding domain actions
12 - Chapter 2. AI as a part of a larger product
13 - Chapter 2. Overview of AI capabilities
14 - Chapter 2. Introducing unicorns
15 - Chapter 2. Exercises
16 - Chapter 3. Choosing your first AI project
17 - Chapter 3. Prioritizing AI projects
18 - Chapter 3. Measuring AI project success with business metrics
19 - Chapter 3. Your first project and first research question
20 - Chapter 3. Pitfalls to avoid
21 - Chapter 3. Using your gut feeling instead of CLUE
22 - Chapter 4. Linking business and technology
23 - Chapter 4. Linking business problems and research questions
24 - Chapter 4. A metric you don�t understand is a poor business metric
25 - Chapter 4. Measuring progress on AI projects
26 - Chapter 4. Linking technical progress with a business metric
27 - Chapter 4. Why is this not taught in college
28 - Chapter 4. Organizational considerations
29 - Chapter 5. What is an ML pipeline, and how does it affect an AI project
30 - Chapter 5. Challenges the AI system shares with a traditional software system
31 - Chapter 5. Example of ossification of an ML pipeline
32 - Chapter 5. How to address ossification of the ML pipeline
33 - Chapter 5. Why we need to analyze the ML pipeline
34 - Chapter 5. What�s the role of AI methods
35 - Chapter 5. Balancing data, AI methods, and infrastructure
36 - Chapter 6. Analyzing an ML pipeline
37 - Chapter 6. Economizing resources - The E part of CLUE
38 - Chapter 6. How to interpret MinMax analysis results
39 - Chapter 6. What if your ML pipeline needs improvement
40 - Chapter 6. How to perform an analysis of the ML pipeline
41 - Chapter 6. Performing the Max part of MinMax analysis
42 - Chapter 6. Estimates and safety factors in MinMax analysis
43 - Chapter 6. Dealing with complex profit curves
44 - Chapter 6. FAQs about MinMax analysis
45 - Chapter 7. Guiding an AI project to success
46 - Chapter 7. Performing local sensitivity analysis
47 - Chapter 7. We�ve completed CLUE
48 - Chapter 7. Advanced methods for sensitivity analysis
49 - Chapter 7. How to address the interactions between ML pipeline stages
50 - Chapter 7. One common objection you might encounter
51 - Chapter 7. How to analyze the stage that produces data
52 - Chapter 7. How your AI project evolves through time
53 - Chapter 7. Concluding your AI project
54 - Chapter 8. AI trends that may affect you
55 - Chapter 8. AI in physical systems
56 - Chapter 8. IoT devices and AI systems must play well together
57 - Chapter 8. AI doesn�t learn causality, only correlations
58 - Chapter 8. How are AI errors different from human mistakes
59 - Chapter 8. AutoML is approaching
60 - Chapter 8. Guiding AI to business results
61 - Appendix B. Exercise solutions
62 - Appendix B. Answers to chapter 2 exercises
63 - Appendix B. Answers to chapter 3 exercises
64 - Appendix B. Answers to chapter 6 exercises
65 - Appendix B. Answers to chapter 7 exercises