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Machine learning: Koombea TechTalks

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This deck was presented as part of a company initiative, #TechTalks, aimed to provide a space for the sharing and exploration of topics of interest in the industry.

Presented by: Javier Fonseca, Back-end developer

Veröffentlicht in: Technologie
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Machine learning: Koombea TechTalks

  1. 1. Machine Learning ... or how Google predicts everything about you By: Javier Fonseca Back-end developer
  2. 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. 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. 4. How is Google able to achieve such a noble goal? ● They store tons of information. ● They apply Machine Learning to it.
  5. 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. 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. 7. Okay okay... ... what is that machine learning thing they do?
  8. 8. Machine Learning is... "... the field of study that gives computers the ability to learn without being explicitly programmed" Arthur Samuel, 1959
  9. 9. 1959?!?! Then why is it such a buzzword now?
  10. 10. Couple of reasons...
  11. 11. Computer Speed Source: Google (of course)
  12. 12. Emergence of Internet
  13. 13. Types of Machine Learning ● Supervised learning ● Unsupervised learning
  14. 14. Supervised Learning Given labeled data, perform: ● Function Approximation ● Regression ● Classification
  15. 15. Supervised Learning Common algorithms: ● Linear regression ● Logistic regression ● Support Vector Machines ● Naive Bayes ● Neural Networks
  16. 16. Unsupervised Learning Given unlabeled data, detect: ● Patterns, structure ● Anomalies ● Clusters (groups)
  17. 17. Unsupervised Learning Common algorithms: ● k-means ● Outlier detection ● Neural Networks
  18. 18. Neural Networks
  19. 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. 20. Further Information Clouds ● Google Cloud Machine Learning ● Microsoft Azure Machine Learning ● Amazon AI ● IBM Watson
  21. 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. 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. 23. Further Information Communities, social networks ● Big Data Colombia (Slack) ● Machine Learning Colombia (Facebook Group) ● Kaggle Competitions
  24. 24. Further Information And more...
  25. 25. Thank you!