This document provides an overview of machine learning concepts and how Python can be used for machine learning. It discusses misconceptions about machine learning, the concepts of supervised and unsupervised learning, common machine learning algorithms like k-nearest neighbors and neural networks. It also shows how Python has useful tools for tasks like classification, dimensionality reduction, and data visualization that make it a good choice for machine learning applications.

1. Machine Learning
in Python
Dhiana Deva - PyLadies Stockholm

2. Agenda
Goal: Encourage you to use Machine Learning… today!
‣ Aboutme
‣ MachineLearning
Misconceptions
Concepts
Problems andAlgorithms
‣ ML+Python
PythonToolbox
Code Snippets
Spotify<3 Python

3. About me
Electronics Engineering, Software
Development, Machine Learning… Why not?

4. Machine
Learning
It is all about learning

5. Misconceptions
Too difficult
Big upfront investments
Needs supercomputers
Only for PhDs from MIT
Data Silo
Takes too long to pay off
ƃ

6. Reality
♥

7. Feature Extraction
Item {
Feature 1
Feature 2
Feature 3
…
Feature N
Feature2
0
8
16
24
32
40
Feature 1
0 10 20 30 40 50 60 70 80 90

8. Supervised Learning
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
3
2
…
5
20, 39, 59, 68 3
♥
Items Features Labels Algorithm
New Item PredictionFeatures Model

9. Regression
921 37
23 2487 1541
21 2121 21
?

10. Classification
BA B
A AB AB
A AB B
?

11. Supervised Learning Algorithms
K-Nearest Neighbors Neural Networks
DecisionTree Random Forest

12. Detecting particles
Online electron detection
based on more than 1500
detector cells using
Neural Networks
(GeV)TE
0 10 20 30 40 50 60 70 80
o(%)a
~
Prob.deRejeic
20
30
40
50
60
70
80
90
100
Ringer
T2Calo
smicosoC
η
-2 -1 0 1 2
o(%)a
~
Prob.deRejeic
80
85
90
95
100
Ringer
T2Calo
φ
-3 -2 -1 0 1 2 3
o(%)a
~
Prob.deRejeic
90
92
94
96
98
100
Ringer
T2Calo

13. Unsupervised Learning
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
20, 39, 59, 68
♥
Items Features Algorithm
New Item
Better
RepresentationFeatures Model

14. Clustering

15. Dimensionality Reduction

16. Unsupervised Learning Algorithms
K-Means Self-Organising Maps t-SNE

17. Visualizing employees
Visualization of 2000+
employees described by
200+ skills after reducing
dimensionality using the
t-SNE algorithm

18. ML + Python
Amatch made in heaven!

19. Python Toolbox

20. Classification
>>> classifier = RandomForestClassifier().fit(X_train,Y_train)
>>>Y_test = classifier.predict(X_test)
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
A
B
…
B
20, 39, 59, 68 A
♥
Items Features Labels Algorithm
New Item PredictionFeatures Model

21. 23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19
♥
Items Features Algorithm
Better
RepresentationModel
Dimensionality Reduction
>>> embedding =TSNE().fit_transform(X_train)

22. Data Visualization
>>> Scatter(df, x=‘x1’, y=‘x2’, color=‘label')

23. Spotify <3 Python

24. Thank you.
@dhianadeva