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mlcourse.ai, introduction, course overview
This document summarizes the open machine learning course offered by OpenDataScience. The course consists of 10 lectures covering basic machine learning algorithms and their applications. Students gain theoretical understanding through lectures and practice through assignments, competitions, and individual projects. The course emphasizes hands-on practice, explores real-world applications, and builds a vibrant community of learners. All materials are available online through GitHub and communication occurs through the ODS Slack channel #mlcourse_ai. Top students are recognized on a wiki page and the toolbox includes Python, Jupyter notebooks, and popular ML libraries.
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- 1. mlcourse.ai Open Machine LearningCourse by OpenDataScience Yury Kashnitskiy (@yorko) Data Scientist @ KPN, Amsterdam
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- 4. mlcourse.ai. What wehave for you
- 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. What makes itdifferent • Lots and lots of practice • Theoretical understanding of applied techniques • Delving into competitions • Your own projects • Really vibrant community!
- 7. Roadmap/logistics • All communicationin 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. Toolbox • Python • Jupyternotebooks • GitHub • Docker (optional) • Other libs like Vowpal Wabbit & Xgboost • Instructions https://mlcourse.ai/prerequisites
- 9. Lecture 1 • Dataanalysis with Pandas • Practice on first steps after getting data
- 10. Lecture 2 • Visualdata analysis with Pandas and Seaborn • Crucial plots for feature exploration • Practice on «drawing»
- 11. Lecture 3 • Foundationsof Machine Learning • Supervised learning • Decision trees • k Nearest Neighbours • Practice: first steps with Scikit-learn
- 12. Lecture 4 • Linearclassification models • Regularization • Cross-validation • Practice on logistic regression for a "real-world" task
- 13. Lecture 5 • Ensembles,random forest • Feature importance • Practice on random forest and assessing feature importance
- 14. Lecture 6 • Regressiontask • Linear and non-linear regression models • Practice on grasping core ideas behind linear regression
- 15. Lecture 7 • UnsupervisedLearning • Principal Component Analysis • Clustering • Practice: clustering Samsung Galaxy S3 sensor data into types of human activity
- 16. Lecture 8 • StochasticGradient Descent & Online learning • Learning with a couple GB of data • Vowpal Wabbit • Extracting simple features from texts • Practice: text classification
- 17. Lecture 9 • Timeseries • Classical and modern approaches • Practice: ARIMA model, Facebook Prophet
- 18. Lecture 10 • Gradientboosting: a modern view • Theoretical basis for gradient boosting • Best implementations • Practice: beating a baseline in a Kaggle Inclass competition Regularization?
- 19. Assignments • Full versionsare 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. 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. Individual projects • Throughoutthe whole course • Straightforward instructions • Your own data or just Kaggle Datasets • Peer review • Very cool experience More info here https://mlcourse.ai/roadmap
- 22. Project "Alice" • Asubstitute 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. More info inSlack #mlcourse.ai, pinned items Good luck! https://mlcourse.ai/news