The document explores how Google utilizes machine learning to organize and make accessible the vast amount of information it collects from users. It outlines the types of machine learning methods employed, such as supervised and unsupervised learning, and lists various algorithms and software resources for implementing these techniques. Additionally, it highlights Google's commitment to providing relevant ads based on user data while maintaining a user-centric mission.

1. Machine Learning
... or how Google predicts
everything about you
By: Javier Fonseca
Back-end developer

2. What is Google?
Is it an Internet search company?
Is it an online
advertising service
company?
As per the Wikipedia definition, it is an Internet-related services company.
... a pretty broad definition, isn't it?

3. But what about their mission?
“Organize the world’s information and make it universally
accessible and useful.”
Source: https://www.google.com/intl/en/about/our-company/

4. How is Google able to achieve such a noble
goal?
● They store tons of information.
● They apply Machine Learning to it.

5. Some information Google tracks about users
● Searches
● "Ok Google" activity
● Web browsing activity
● Location history
● YouTube searches
● YouTube "Not Interested" feedback
● YouTube watches
● Mobile apps you used
● Google Ads settings
Source: https://myactivity.google.com/

6. How does Google use that information?
● To improve their services.
● And... to sell your personal data? NO!
● But... to make ads relevant.
Source: https://privacy.google.com/how-ads-work.html
Source: https://privacy.google.com/your-data.html

7. Okay okay...
... what is that machine
learning thing they do?

8. Machine Learning is...
"... the field of study that gives
computers the ability to learn without
being explicitly programmed"
Arthur Samuel, 1959

9. 1959?!?!
Then why is it such a buzzword now?

10. Couple of reasons...

11. Computer
Speed
Source: Google (of course)

12. Emergence of Internet

13. Types of Machine Learning
● Supervised learning
● Unsupervised learning

14. Supervised Learning
Given labeled data, perform:
● Function Approximation
● Regression
● Classification

15. Supervised Learning
Common algorithms:
● Linear regression
● Logistic regression
● Support Vector Machines
● Naive Bayes
● Neural Networks

16. Unsupervised Learning
Given unlabeled data, detect:
● Patterns, structure
● Anomalies
● Clusters (groups)

17. Unsupervised Learning
Common algorithms:
● k-means
● Outlier detection
● Neural Networks

18. Neural Networks

19. Further Information
Software resources
● scikit-learn: machine learning in Python
● Anaconda: distribution of Python and R for large scale data
processing
● TensorFlow: An open-source software library for Machine
Intelligence
● Andrei Beliankou: Machine Learning with Ruby (curated list of links)

20. Further Information
Clouds
● Google Cloud Machine Learning
● Microsoft Azure Machine Learning
● Amazon AI
● IBM Watson

21. Further Information
Basic courses
● Adam Geitgey: Machine Learning is Fun! (8-part blog)
● Udacity Picodegree: a friendly introduction to Machine Learning
● Udacity: Intro to Machine Learning
● Google: Machine Learning recipes with Josh Gordon
● Coursera: Machine Learning

22. Further Information
Advanced Courses
● University of Stanford: CS231n Convolutional Neural Networks for
Visual Recognition
● fast.ai: Practical Deep Learning for Coders
● MIT Press: Deep Learning Book
● Udacity: Deep Learning by Google
● Coursera: Deep Learning specialization

23. Further Information
Communities, social networks
● Big Data Colombia (Slack)
● Machine Learning Colombia (Facebook Group)
● Kaggle Competitions

24. Further Information
And more...

25. Thank you!