1.01_week-1-introduction
2.02_machine-learning
3.03_what-is-data
4.04_the-terminology-of-ai
5.05_what-makes-an-ai-company
6.06_what-machine-learning-can-and-canno
7.07_more-examples-of-what-machine-learn
8.08_non-technical-explanation-of-deep-l
9.09_non-technical-explanation-of-deep-l
10.01_week-2-introduction
11.02_workflow-of-a-machine-learning-pro
12.03_workflow-of-a-data-science-project
13.04_every-job-function-needs-to-learn-
14.05_how-to-choose-an-ai-project-part-1
15.06_how-to-choose-an-ai-project-part-2
16.07_working-with-an-ai-team
17.08_technical-tools-for-ai-teams-optio
18.01_week-3-introduction
19.02_case-study-smart-speaker
20.03_case-study-self-driving-car
21.04_example-roles-of-an-ai-team
22.05_ai-transformation-playbook-part-1
23.06_ai-transformation-playbook-part-2
24.07_ai-pitfalls-to-avoid(
25.08_taking-your-first-step-in-ai
26.09_survey-of-major-ai-application-are
27.10_survey-of-major-ai-techniques-opti
28.01_week-4-introduction
29.02_a-realistic-view-of-ai
30.03_discrimination-bias
31.04_adversarial-attacks-on-ai
32.05_adverse-uses-of-ai
33.06_ai-and-developing-economies
34.07_ai-and-jobs
35.08_conclusion
