1. Understanding ML Project Phases.

2. Understanding the typical challenges on an ML Project with respect to:

(a) Data Volume 
(b) Data Quality 
(c) Feature Engineering 
(d) Modeling, and 
(e) Results

3. How to interpret and present model results.