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Study mode:
on
1
Intro
2
Who am I
3
Open source projects
4
Machine learning
5
Decision trees
6
Predicting spam
7
Bait logic
8
significance of machine learning
9
how I came to this idea
10
example data
11
main takeaway
12
another example
13
x2y problems
14
ImageNet
15
Insurance
16
Academic Data
17
Localisation
18
Transfer Learning
19
Image Data
20
Compliance
21
Neural Network
22
Cryptocurrency
23
Connecting to ML
24
Python for ML
25
X2Y
26
Reading the data
27
Bundle Preprocessing
28
In the end
29
Questions
Description:
Explore the world of machine learning through this insightful 39-minute conference talk from EuroPython 2018. Gain valuable tips, tricks, and warnings on when to effectively implement machine learning solutions. Discover the core concepts of artificial intelligence and learn how to critically evaluate proposed machine learning projects. Delve into real-world applications, including open-source Python projects and cryptocurrency trading, as the speaker shares challenges and findings from practical experiences. Understand decision trees, spam prediction, and the significance of machine learning in various domains such as image recognition, insurance, and academic data. Explore topics like transfer learning, neural networks, and data preprocessing while gaining insights into the practical implementation of machine learning using Python.

When to Use Machine Learning - Tips, Tricks and Warnings

EuroPython Conference
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