[MUSIC]
Hello, and welcome.
My name is Dimitri, and
I'm happy to see you are interested in competitive data science.
Data science is all about machine learning applications.
And in data science, like everywhere else, people are looking for
the very best solutions to their problems.
They're looking for the models that have the best predictive capabilities,
the models that make as few mistakes as possible.
And the competition for one becomes an essential way to find such solutions.
Competing for the prize, participants push through the limits,
come up with novel ideas.
Companies organize data science competitions to get top quality models for
not so high price.
And for data scientists, competitions become a truly unique
opportunity to learn, well, and of course win a prize.
This course is a chance for you to catch up on the trends in competitive data
science and learn what we, competition addicts and at the same time,
lecturers of this course, have already learned while competing.
In this course, we will go through competition solving process step by
step and tell you about exploratory data analysis, basic and advanced feature
generation and preprocessing, various model validation techniques.
Data leakages, competition's metric optimization, model ensembling,
and hyperparameter tuning.
We've put together all our experience and created this course for you.
We've also designed quizzes and
programming assignments to let you apply your newly acquired skills.
Moreover, as a final project, you will have an opportunity to compete with
other students and participate in a special competition,
hosted on the world's largest platform for data science challenges called Kaggle.
Now, let's meet other lecturers and get started.