Price - Paid:
If you want to break into competitive data science, then this course is for you! Participating in
predictive modelling competitions can help you gain practical experience, improve and harness
your data modelling skills in various domains such as credit, insurance, marketing, natural
language processing, sales’ forecasting and computer vision to name a few. At the same time
you get to do it in a competitive context against thousands of participants where each one tries
to build the most predictive algorithm. Pushing each other to the limit can result in better
performance and smaller prediction errors. Being able to achieve high ranks consistently can
help you accelerate your career in data science.
In this course, you will learn to analyse and solve competitively such predictive modelling tasks.
When you finish this class, you will:
- Understand how to solve predictive modelling competitions efficiently and learn which of the
skills obtained can be applicable to real-world tasks.
- Learn how to preprocess the data and generate new features from various sources such as
text and images.
- Be taught advanced feature engineering techniques like generating mean-encodings, using
aggregated statistical measures or finding nearest neighbors as a means to improve your
- Be able to form reliable cross validation methodologies that help you benchmark your solutions
and avoid overfitting or underfitting when tested with unobserved (test) data.
- Gain experience of analysing and interpreting the data. You will become aware of
inconsistencies, high noise levels, errors and other data-related issues such as leakages and
you will learn how to overcome them.