#31: Machine Learning with Python and scikit-learn

1

Introduction

00:00 • 2min

2

How I Got Into Python Programming and Scikit Learning

02:08 • 2min

3

Machine Learning and the Future of Data Science

03:43 • 2min

4

The Basics of Machine Learning

06:04 • 2min

5

How to Estimate the Value of Your Home Using Real Estate Data

07:57 • 2min

6

The Novel Uses of Machine Learning

10:12 • 2min

7

The Future of Data Science

11:46 • 2min

8

How to Win a Kaggle Competition Without Doing Anything About the Data

14:08 • 2min

9

How to Use Scikit-Learn to Train a Machine

16:10 • 2min

10

How to Use Machine Learning to Detect Anomalies and Predict Failures

18:30 • 2min

11

Scikit Learn: The Biggest Machine Learning Library

20:53 • 2min

12

Scikit Learn: The First Stable, Fully Stable Release

23:17 • 2min

13

The Linear Model Module in Scikit Learn

25:15 • 2min

14

The Power of Random Forests in Machine Learning

27:14 • 3min

15

How Scikit Learn Supports Dimensionality Reduction

30:19 • 2min

16

How to Predict Multiple Supervised Models

32:09 • 2min

17

The Future of Deep Learning

34:13 • 2min

18

Iris: A Dataset for Classifying Plants

36:17 • 2min

19

The Future of Machine Learning

37:57 • 2min

20

Machine Learning and Artificial Intelligence

39:54 • 2min

21

How to Get Started With Scikit-Learn

41:32 • 2min

22

How to Maintain a Cool Python Library

43:48 • 2min

23

Machine Learning for Brain Science

45:22 • 2min

24

Talk Python to Me: Alexandria Graham Ford

47:19 • 2min