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mlcourse.ai, introduction, course overview

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Introduction to Open Machine Learning Course by OpenDataScience. https://mlcourse.ai

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mlcourse.ai, introduction, course overview

  1. 1. mlcourse.ai Open Machine Learning Course by OpenDataScience Yury Kashnitskiy (@yorko) Data Scientist @ KPN, Amsterdam
  2. 2. OpenDataScience. DataFest
  3. 3. OpenDataScience. Kaggle
  4. 4. mlcourse.ai. What we have for you
  5. 5. Syllabus • 10 lectures • Basic ML algorithms and their applications • Assignments and in-class practice • Competitions • Individual projects • Tutorials More info here https://mlcourse.ai/roadmap
  6. 6. What makes it different • Lots and lots of practice • Theoretical understanding of applied techniques • Delving into competitions • Your own projects • Really vibrant community!
  7. 7. Roadmap/logistics • All communication in ODS Slack, #mlcourse_ai • https://mlcourse.ai/roadmap • 10 assignments – ~10 credits each • Projects, competitions, tutorials – up to 40 crd. each • Current rating is here https://goo.gl/TGGr3b • All materials are stored on GitHub https://github.com/Yorko/ mlcourse.ai and https://mlcourse.ai • Top-100 participants will be mentioned on a special Wiki page
  8. 8. Toolbox • Python • Jupyter notebooks • GitHub • Docker (optional) • Other libs like Vowpal Wabbit & Xgboost • Instructions https://mlcourse.ai/prerequisites
  9. 9. Lecture 1 • Data analysis with Pandas • Practice on first steps after getting data
  10. 10. Lecture 2 • Visual data analysis with Pandas and Seaborn • Crucial plots for feature exploration • Practice on «drawing»
  11. 11. Lecture 3 • Foundations of Machine Learning • Supervised learning • Decision trees • k Nearest Neighbours • Practice: first steps with Scikit-learn
  12. 12. Lecture 4 • Linear classification models • Regularization • Cross-validation • Practice on logistic regression for a "real-world" task
  13. 13. Lecture 5 • Ensembles, random forest • Feature importance • Practice on random forest and assessing feature importance
  14. 14. Lecture 6 • Regression task • Linear and non-linear regression models • Practice on grasping core ideas behind linear regression
  15. 15. Lecture 7 • Unsupervised Learning • Principal Component Analysis • Clustering • Practice: clustering Samsung Galaxy S3 sensor data into types of human activity
  16. 16. Lecture 8 • Stochastic Gradient Descent & Online learning • Learning with a couple GB of data • Vowpal Wabbit • Extracting simple features from texts • Practice: text classification
  17. 17. Lecture 9 • Time series • Classical and modern approaches • Practice: ARIMA model, Facebook Prophet
  18. 18. Lecture 10 • Gradient boosting: a modern view • Theoretical basis for gradient boosting • Best implementations • Practice: beating a baseline in a Kaggle Inclass competition Regularization?
  19. 19. Assignments • Full versions are announced during course sessions https:// mlcourse.ai/assignments • Demo versions are found in course repo https:// github.com/Yorko/mlcourse.ai • And in a Kaggle Dataset mlcourse.ai https:// www.kaggle.com/kashnitsky/ mlcourse
  20. 20. Kaggle Inclass • Alice - tracking visited websites to distinguish Alice from all others • Medium - predicting #claps for a story on Medium More info here https://mlcourse.ai/roadmap
  21. 21. Individual projects • Throughout the whole course • Straightforward instructions • Your own data or just Kaggle Datasets • Peer review • Very cool experience More info here https://mlcourse.ai/roadmap
  22. 22. Project "Alice" • A substitute for an individual project if you don't have cool ideas for one • Clear instructions • 6 weeks, 6 notebooks to complete • In cooperation with Yandex and MIPT, specialization "Machine Learning and Data Analysis" • Solutions are not shared Tutorials • Your own tutorials on pretty much any topic around ML & DS • Peer-voted • Nice way to grasp something yourself is to write a tutorial More info here https://mlcourse.ai/roadmap
  23. 23. More info in Slack #mlcourse.ai, pinned items Good luck! https://mlcourse.ai/news

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