mlcourse.ai is one of the largest free machine learning courses run by OpenDataScience, with over 10,000 participants and 3,000 alumni. The course consists of 4 modules covering topics like data analysis, tree-based models, linear models, and other techniques. Each module includes quizzes, assignments applying the concepts, and practice competing in Kaggle competitions to beat baselines.

1. mlcourse.ai
by OpenDataScience
lead by Yury Kashnitsky (@yorko)

2. In 4 lines
• One of the largest open & free ML
courses
• Run for ~3 years by ODS.ai team
• >10k participants, ~3k alumni
• Good start for a junior DS position
A podcast about mlcourse.ai

3. Fall 2019 design choice
• 4 modules
• Quizzes (theory)
• Assignments (practice)
• Kaggle (practice)

4. Module 1. Data Analysis
The golden rule: in your task, always start
with Exploratory Data Analysis.
Plan:
• 1 assignment
• Dota 2 winner prediction EDA -
Kaggle

5. Module 2. Tree-based models
Random Forest and gradient boosting
are wheelhorses of practical ML
Plan:
• 1 assignment in 2 parts
• 1 quiz
• Beating a baseline in a Kaggle
competition

6. Module 3. Linear models
Linear models are most wide-spread in
whole of ML, econometrics, statistics etc.
Plan:
• 1 quiz
• Beating a baseline in a Kaggle
competition + actually competing

7. Module 4. “The rest”
How to scale to Gbs with simple models (Vowpal
Wabbit), time series, unsupervised learning
Plan:
• 1 quiz (all topics)
• 1 assignment (time series)
• Beating a baseline in a Kaggle competition
with ~10 Gb of data

8. Roadmap
What you can follow:
• #mlcourse_ai_news
• mlcourse.ai/roadmap
• Google calendar

9. Questions
Cool stories
False Promises