Building a Business Use Case for Data Science Products | Data Science | Community Webinar
Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.
Outline:
• Why starting with technology is the wrong approach? Ask a business question and work your way backward
• Choosing the right machine learning algorithm for your business problem
• High-level thinking pr…
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Chapters (21)
Introduction
0:46
About the Speaker
1:10
Why This Talk?
2:05
Is a Highly Accurate Model Always Desirable?
2:23
The 'Data' Question
3:11
The 'Metric' Question
4:43
The 'Repeatability Question
6:31
The 'Business' Question
7:39
The 'Impact' Question
11:16
Modern ML Tools - A Blessing or a Curse?
13:20
Machine Learning Model Life Cycle
22:11
What is your biggest pain point?
23:07
Data beats algorithm
24:07
Dispelling a Common Myth
24:29
The 80/20 rule
25:09
Beg, borrow and steal
25:44
Building the model is only a small piece of the puzzle
26:43
The Importance of Data
27:45
Predictability of your model
27:56
Further Evaluation Data Set
29:23
Increasing the exposure of your model
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