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.

1. mlcourse.ai
Open Machine Learning Course
by OpenDataScience
Yury Kashnitskiy (@yorko)
Data Scientist @ KPN, Amsterdam

2. OpenDataScience. DataFest

3. OpenDataScience. Kaggle

4. mlcourse.ai. What we have 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 it different
• Lots and lots of practice
• Theoretical understanding of
applied techniques
• Delving into competitions
• Your own projects
• Really vibrant community!

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. Toolbox
• Python
• Jupyter notebooks
• GitHub
• Docker (optional)
• Other libs like Vowpal Wabbit & Xgboost
• Instructions https://mlcourse.ai/prerequisites

9. Lecture 1
• Data analysis with Pandas
• Practice on first steps after
getting data

10. Lecture 2
• Visual data analysis with
Pandas and Seaborn
• Crucial plots for feature
exploration
• Practice on «drawing»

11. Lecture 3
• Foundations of Machine
Learning
• Supervised learning
• Decision trees
• k Nearest Neighbours
• Practice: first steps with
Scikit-learn

12. Lecture 4
• Linear classification 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
• Regression task
• Linear and non-linear
regression models
• Practice on grasping core
ideas behind linear regression

15. Lecture 7
• Unsupervised Learning
• Principal Component Analysis
• Clustering
• Practice: clustering Samsung
Galaxy S3 sensor data into
types of human activity

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. Lecture 9
• Time series
• Classical and modern
approaches
• Practice: ARIMA model,
Facebook Prophet

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. 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. 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
• 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. 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. More info in Slack
#mlcourse.ai, pinned items
Good luck!
https://mlcourse.ai/news