Data Science Happy Hour 51

1

Introduction

00:00 • 3min

2

Ahad Tet E Dodif Anybody Asks Questions?

02:40 • 2min

3

How Are You Managing Your Experiments Before?

04:14 • 3min

4

How to Be a Data Scientist?

07:17 • 4min

5

What Methodology Is Best for Your Business?

10:57 • 3min

6

What Are Some Mental Models That You Shouldn't Use?

13:45 • 2min

7

Inversion for Binary Classification Problems

16:08 • 3min

8

Integrating Blissfuls

19:09 • 2min

9

How Do We Get to Mental Models for the Thin It's Not a Technical Skill, Right?

21:25 • 4min

10

I Love the Shod but Eric Gets Back, O Course.

25:26 • 4min

11

How Much of Probability Do You Understand?

29:02 • 2min

12

What's the Probability of Something Happening?

30:52 • 3min

13

The Probability of an Account Being a Fraud

34:22 • 5min

14

Using the Igenvectors in Pc

39:34 • 2min

15

The Machine Learning Development Pipe Lane

42:04 • 3min

16

Is It Reasonable to Talk About Your Igon Space Features as a Proxies for Their Real Features?

45:07 • 2min

17

Using Pc a in a Data Model?

46:45 • 5min

18

What Are People Using These Days to Learn Data Science?

52:11 • 2min

19

How Do You Identify How You Best Learn?

54:24 • 2min

20

Learning Machine Learning - What Is It Like?

56:27 • 6min

21

How Can I Simplify the Problem?

01:02:33 • 4min

22

Predictive Accuracy - What Is It?

01:06:09 • 2min

23

Precision Is a Good Thing

01:07:55 • 2min

24

The Importance of Af One Score in a Classification Problem

01:09:49 • 3min

25

Is It True Positive or False Positive?

01:12:20 • 5min

26

Is There a Way to Reduce False Negatives?

01:16:51 • 3min

27

Graph Data Basin

01:20:13 • 4min

28

The Data Analysts Roll for Marketing and Story Telling

01:24:13 • 6min

Data Science Happy Hour 51 | 24SEP2021