Lecture 1 Overview
Introduction to ML competitions
Lecture 2 Tips
Becoming more fluent
Lecture 3 Results of Kaggle 1
Show me the Money: Group Presentation for Kaggle 1
Lectures 4-5 Overview
Getting stronger
- Lecture 4: Hyperparameters tuning
- Lecture 5: Model stacking and ensembling
- Kaggle Tutorial on Time Series
- Lecture: WiDS Global Datathon2026
- Lecture: WiDS Global Datathon 2025 Insights
- Homework 2: Kaggle 2
- Learning 2: WiDS Datathon 2025
- Lecture: Birdclef 2026
- Lecture: Birdclef 2025 Insights
- Homework 3: Kaggle 3
- Learning 3: BirdCLEF+ 2025
Lecture 6 Becoming a master
Show me the Money: Group Presentation for Kaggle 2
Lecture 7-8 Result of Kaggle 3
Show me the Money: Group Presentation for Kaggle 3And Farewell Party 😊
Homeworks
Check this part regularly to avoid being late on your homework.
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction to ML competitions-
- Lecture 2: Metrics
- Lecture 3 Part One: EDA + Feature Selection
- Lecture 3 Part two: A blending model
- Lecture 4: Validation
- Lecture 5: Hyperparameters tuning
- Lecture 6: Model stacking
- Lecture 7: Time Series Course on Kaggle
- Lecture: WiDS Global Datathon2026
- Lecture: WiDS Global Datathon 2025 Insights
- Lecture: Birdclef 2026
- Lecture: Birdclef 2025 Insights
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