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How to Win a Data Science Competition: Learn from Top Kagglers

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

4.5

Course Description

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 predictions.- 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. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them.Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them.Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM.- Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.Do you have technical problems? Write to us: coursera@hse.ru

Time: 49h

Instructor Details

Dmitry Ulyanov

4.5 Rating
149 Reviews

Dmitry received his master's degree at Moscow State University. His major was machine learning and mathematical methods of forecasting. Dmitry is now studying for his PhD degree at Skoltech and working for Yandex as Research Scientist. He also serves as teacher assistant at deep learning class at Skoltech university and School of Data Analysis. In the world of competitive data science Dmitry got prizes more than 10 times.

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4.5

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By Diego T B on 7-Nov-19

This is an awesome course! I really learned a lot from this top kagglers. I just have one recommendation. I think some sessions were very though and difficult to catch: the data leakage part and the Kappa metric. Try to make this even much easier.

By Margarita C on 7-Nov-19

This course is awesome. The more effort I invested in it - the more results I got, and I feel like I haven't reached the limit. The course is very thought-out, the tests and programming assignments are great, teachers are inspiring. I enjoyed every part of it!

By charles l on 4-Nov-19

Great course! You will learn very advanced modeling techniques that are not only useful for data competitions but also real machine learning applications.

By Yotam S on 26-Oct-19

Amazing course. Teaches the theoretical aspects of ML in within a practical point of view. Enables use to improve your models by understanding the framework much better. Not recommended as first ML course, but definitely as an advanced one.

By Alouini M Y on 18-Oct-19

A very challenging ML course but worth all the efforts. This course contains various interesting analyses of techniques to get better at ML modelling: from mean encoding (and how to do properly) to nearest neighbors features and model stacking. This is very useful for Kaggle challenges specifically but is valuable for day-to-day data science tasks more generally.

By jagannadha R b on 17-Oct-19

One of the best course with the top kagglers sharing their experience of solving the most complex data science problems. ! Thanks to Courseera ! Jagan

By Yury A on 9-Oct-19