The Data Scientist Show - Daliana Liu

The power of error analysis, tree models for search relevancy, what ChatGPT means for data scientists - Sergey Feldman - The Data Scientist Show #059

Jan 24, 2023
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Episode notes
1
Introduction
00:00 • 2min
2
Machine Learning for Search Algorithms - What's the Challenge?
02:29 • 5min
3
Machine Learning for Biomedicine - The Machine Learning Model for Biomedicine
07:15 • 2min
4
Using Tree-Based Models in Search Engines
08:56 • 4min
5
Machine Learning
12:33 • 3min
6
How to Do Error Analysis in a Short Cycle?
15:36 • 2min
7
How to Find the Lowest Hanging Bugs in Your Data
17:48 • 2min
8
Nested Cross-Validation for Machine Learning
19:21 • 2min
9
How Do You Evaluate a Model?
21:35 • 2min
10
Why You Shouldn't Label the Data in the Training Set
23:40 • 2min
11
Is There a Way to Validate a Big Data Set?
25:52 • 3min
12
Is There a Common Theme for Machine Learning?
28:50 • 2min
13
Error Analysis for Deep Learning Models
30:44 • 2min
14
How Do You Know if You're Not Overfitting the Model?
32:45 • 3min
15
Can We Use ML to Do Something Good?
35:57 • 4min
16
How Do You Decide Which One to Move On?
39:38 • 2min
17
Machine Learning Models Perform Better Than Linear Models
41:16 • 2min
18
Is There a User Researcher Working With You on This?
43:10 • 2min
19
How to Communicate With Healthcare Professionals?
45:20 • 2min
20
ML for Healthcare - What Made You Want to Work on ML for Healthcare?
47:39 • 3min
21
Chat GBTA - What's the Biggest Problem?
50:20 • 4min
22
Chat GPT for Therapy?
53:57 • 3min
23
How Did You Get Into Machine Learning?
57:00 • 5min
24
You Don't Like Having a Boss?
01:02:13 • 2min
25
Is That the Right Thing to Say to People?
01:03:45 • 2min
26
Do You Value Facts or People's Feelings?
01:05:48 • 2min
27
How Do You Take Care of Other People's Emotions?
01:07:31 • 2min
28
The Future of Machine Learning?
01:09:02 • 5min
29
XGBoose Models vs Light GBM Models?
01:13:40 • 2min
30
How Do You See Your Career Grow?
01:15:48 • 4min