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.

( 149 Reviews )

Created by: Dmitry Ulyanov

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Overall Score : 90 / 100

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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By ÃÂžÃÂ¼ÃÂµÃÂ»Ã‘ÂŒÃ‘Â‡Ã‘ÂƒÃÂº ÃÂ® Ã on 18-May-18

a little too difficult for new pandas learner, some quizzes are confusing, the reply from the teachers is slow.

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 Setia B on 26-Sep-19

Amazing Course. Thanks!

By Heidi P on 16-Apr-18

Is amazing and so useful, congrats!

By Simon M on 31-Dec-17

Great course not just for competing in Kaggle, but also for giving a deeper understanding towards other things in machine learning besides the algorithms

By FABIAN on 24-Dec-17

I really liked the course, though there were some problems:- during the first weeks not everything in tests is told in lectures. I fully realize that this course/specialization is advanced. But a lot of things we have to self-learn, which usually isn't a point of coursera courses;- Coursera Hub is a mess - if you start doing an assignment and the authors change something, than the only way to get the newer version is to take it from Github. This is inconvenient;- at last the final task: the dataset is too big, even Macbook Pro with 16 Gb Ram takes a lot of time to complete it. Also authors wanted people to try different ways of doing this assignment, but it doesn't work well in coursera peer-graded format;Otherwise the course is great!+ I learned a lot of new things and got a deeper understanding of some things I knew;+ after completing quizes we have detailed explatations - this is really cool;+ explanations of validation and metrics are very good;+ and the section about metrics is very interesting by itself - I rarely thought long about these things previously;+ lectures on mean-encoding were very good, and the programming assignment was excellent;+ it was quite interesting to learn more about hyperparameter tuning;+ feature engineering section is really useful;+ I want to pay a special attention to the additional task - we need to write an algorithm which uses KNN to create new features (distance based metrics and others). It had great practical value. Also I learned to use multiprocessing module, and used this knowledge at my job;+ previously I only heard about staking and booksting technics, but didn't have an opportunity to use them. Now I can do it thanks to the section.So the course it really great, and its value isn't limited by Kaggle - knowledge and skills acquired in this course can be used for job and other applications.

By Karl B on 20-May-18

Really a Great Course, with a lot of informations summarized in short time.

By Caroline D on 4-Dec-17

I can get practical know-how from this lecture

By Francis D on 17-Apr-18

Finally an advanced and comprehensive course in data science! Straight to the point with an extremely useful guidance on how to apply and analyse predictive models!

By CHAN W F P on 12-Jul-18

Many new and useful techniques in this course.

By J R on 29-Mar-19

It is diffifult but when you reach the end, you are glad that you were able to finish it. Because I gained a lot of knowledge and best practices. There is a lot of work but it helps you sharpen your brain. I recommend to work simultaneously on project because otherwise it will be difficult to finish it..

By Mohammad A on 25-Mar-19