Kaggle Competitions Masterclass
Read a competition, build a baseline, climb the leaderboard.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Decode a competition's data, metric and rules and turn them into a plan for the first day
- Run exploratory analysis that surfaces leakage, distribution shift and strong features quickly
- Produce a valid baseline submission with a reusable pipeline in under an hour
- Build a cross-validation scheme whose scores track the private leaderboard
- Generate and test feature ideas systematically and keep only the ones that help
- Blend and stack models for the final submission and publish a notebook people learn from
About this course
This Kaggle competitions course is for data scientists and students who want to use competitions to sharpen their skills, build a public portfolio and stand out to hiring managers. You will learn how to read a competition page and metric, run fast exploratory data analysis that finds signal, ship a baseline submission in the first hour, and design a validation scheme that tracks the private leaderboard rather than the public one. You will then generate feature ideas that move the score, tune gradient boosting models, blend and stack for the final push, and write notebooks that earn upvotes and interviews. Every technique is shown on a tabular competition workflow using pandas, scikit-learn and LightGBM. You should be comfortable with Python and have trained a few models before. By the end you will have a repeatable competition playbook and a notebook that demonstrates it.
Course content
7 lessons- 1Reading a Competition Like a ProDecode the problem, the metric, the data and the rules before writing any code.Preview
- 2Fast EDA That Finds SignalA time-boxed exploration routine that surfaces leaks, shift and strong features in an hour.
- 3A Baseline in the First HourA clean, reusable pipeline that produces a valid submission fast and becomes the harness for every later idea.
- 4Validation That Tracks the LeaderboardDesign folds that mirror the test split so local improvements translate into private leaderboard gains.
- 5Feature Ideas That Move the ScoreA systematic way to generate, test and keep features: aggregations, interactions, target encoding and more.
- 6Ensembling for the Final PushBlend diverse models with out-of-fold predictions, stack with a meta-model, and choose final submissions safely.
- 7Notebooks That Get Upvotes and HiredWrite public notebooks and a competition write-up that teach clearly and showcase your process to employers.