How to Win a Data Science Competition
Learn from Top Kagglers
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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 online 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: [email protected]
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Rating | 4.5★ based on 164 ratings |
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Length | 6 weeks |
Effort | 6-10 hours/week |
Starts | Jan 24 (117 weeks ago) |
Cost | $49 |
From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
Instructors | Dmitry Altukhov, Dmitry Ulyanov, Marios Michailidis, Alexander Guschin, Mikhail Trofimov |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Data Analysis Machine Learning |
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What people are saying
data science
A very complete course, perfect to who wants to learn new techniques for data science.
Amazing Course One of the best data science courses on Coursera!
Learn things through hands-on assignment 膜拜一下 Top Kagglers gently introduce one to Data Science Competitions.
This course is a gold mine of knowledge and tricks for anyone working with the data science toolkit.
Great course, finally an advanced data science track!
Should have been labeled "How to Cheat a Data Science competition".
The real-world data science would be slightly different to this.
They are a real inspiration.Regards - Karthik Just like the course 1 of this specialization, the pace of course 2 on practical data science competition is very fast, therefore the quizzes and assignments are indeed necessary and very helpful (even for the final project).
I can get practical know-how from this lecture Awesome Course for Competitive Data Science!
It is one of the best courses to succeed in the field of competitive data science.
I like the fact that it talk about broad data science topics, and doesn't specialize into one specific domain.
In hintsight after reviewing others, i spend way too much time :P A must for every data scientist, the courses are amazing and you learn a lot a tips.If you have just started data science, you’ll be able to follow the course but you may not understand all the underlying ideas Practice 実際にタスクを与えられたときに、EDAからモデリング、チューニングまで網羅的に触れている点については非常に良かったです。しかし、クイズの解答に疑問が残る点が散見し、かつ、アンサンブル学習のプログラミング課題においてはLightGBMのバージョンが違うと正解にならない点については良くないと感じました。 Very good course Course has good tips, but should not be in this specialization This course requires much time, but gives hardcore experience in practical data science and machine learning.
this course is helpful and important for one who become a data science expert, a lot key skill import in dealing data Really great course, with so great insights!
(2) Helps me to deal with my FOMO (3) I would feel most confident to go for my Data Science or Data Engineering interviews.
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machine learning
But if you interested in machine learning this will not be a problem.
Very interesting, original and revealing materials and tricks to tackle competitive machine learning more efficiently.Sadly, teaching is quite poor and shallow, focusing on personal examples and "I did that and it just worked"-type of experiences that introduce more noise rather than clearing the way to a full understanding of issues during ML competitions (this also includes practical examples).
If it wasn't part of the advanced machine learning specialization, I wouldn't care, but it is.
Also, I am not sure is anybody who is learning Machine Learning possible to do the final task in "6 hours" as solely runs could last for a day... Great course to learn practical skills.
This specialization is much more information-dense than most machine learning MOOCs.
Three reasons (1) Helps me revisit the concepts that I learnt in the machine learning course.
This course is just what I was looking for as I am really interested in competitive Machine Learning and data science.
This course is unique, highly recommended to anyone that wants to push their skill with machine learning, the assignments are excellent and super challenging, after completing the final assignment my understanding how to improve an ml model was better, pushing you to understand how to build a machine learning model to be competitive in Kaggle.All the techniques explained also can help you to create better ml models in general.Thanks very much to all professors for putting together this fantastic course.Looking forward to a more advanced version in the future.
Perfect course in order to learn advanced machine learning techniques for competitions.
A very needed course in not just Kaggle competition but also machine learning.
Even not for the Kaggle tips, the machine learning alone should be reason enough for taking this course.
You will learn very advanced modeling techniques that are not only useful for data competitions but also real machine learning applications.
Brilliant to gain a strong understanding of applying machine learning principles.
This was one of the better online courses I've taken in Machine Learning.
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kaggle competition
Once you get used to Russian accent it goes really well :) Very useful course with full of ideas to apply not only on Kaggle competition but also on the daily projects.
At the beginning, the motivation of taking this course is only to get an better score in Kaggle competition because I major in statistics and am interested in data science.
So i suggest the title be changed to "Basics to start learning how to compete in a Kaggle competition.- Learn by PAST top Competitors" .
In the final, playing kaggle competition was funny.
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final project
Some of the programming tasks & final project are quite hard and time consuming.
Better start working on the first week on the final project on Kaggle and setup your own notebook with big enough memory in AWS/Google/DIY environment, just as the course suggested.
Terrible accent Very nice course and final project is actually challenging and a great learning experience when learner attempts to do it completely on their own without reading forums or looking at examples on Kaggle.
Even if some lessons may seem too theorical, it all comes together during the final project which pushes you to look back and apply what you learned.
And final project is too difficult in sense that my Alienware 16 RAM was not enough, so I had to go to Google Cloud Platform.
I love the painful final project.
Most of that extra time comes from working on the final project, testing things out, etc.
The only reason that I did not give 5 stars is that the task in some assignments could be explained somewhat clearer (would have saved me a lot of time) and especially also the scope of the final project.
The final project, which is a proving ground for the acquired skills, is both an interesting competition to participate in and a real-world-task.
My only real complaint is about the limitations on the frequency of final project submission (even if the submission is ungraded for any reason) which are a little unreasonable.
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tips and tricks
Didn't expect to learn a whole lot of new things, but they have some great tips and tricks here and there that I hadn't come across yet which made it worthwhile.The content is well structured.
The is course packed with machine learning tips and tricks, and for me, this course is more advanced than an average course in my university.
There are lots of useful tips and tricks to get more predictive power from data and model.
Its packed full of tips and tricks and techniques that are well explained and very useful for data science.
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feature engineering
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).
I would highly recommend the course if you want to learn advanced feature engineering and EDA.
I really enjoyed the talks on feature engineering and ensemble methods!
The most time I spent on was to create new features via feature engineering and verify the cross-validation method.
Fascinating, challenging, interesting and useful Teaching the clear work flow with data science project and learned some trick method at feature engineering.
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data scientist
If you are a Data Scientist and want to get another level, this course is for You.
I feel more confident in competing on Kaggle Awesome, Excellent.It gives many tricks for a data scientist.
While some of the material covered is specific to competition, most of it is not, and is very useful for any data scientist.
A must do for any data scientist or aspiring data scientist.
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kaggle competitions
Even though, it revolves around Kaggle competitions which are usually simpler than real-life, this course is full of down-to-earth practical techniques and examples which is really valuable for me.Idea to organize Kaggle competition as a course project is very good.Lectors are easy to follow and nice to listen to.
Taught by experts in the field with a proven track record of outstanding performance in Kaggle competitions.
I remember getting all excited when the instructors would start talking about the Kaggle competitions they personally participated in... only to be left disappointed with how little I learned from their experience.
In addition, after finishing this course I am addicted to Kaggle and am currently in first and second place in some Kaggle competitions.
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but also
But during the processing of learning, I found many important ideas and experience to deal with the real problem and enjoyed the communication with other people from forum and Kaggle, I also aquired some special experience such as peer review, which is not only very fun but also can provide me different aspects to see the problem I'm dealing with again.
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Rating | 4.5★ based on 164 ratings |
---|---|
Length | 6 weeks |
Effort | 6-10 hours/week |
Starts | Jan 24 (117 weeks ago) |
Cost | $49 |
From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
Instructors | Dmitry Altukhov, Dmitry Ulyanov, Marios Michailidis, Alexander Guschin, Mikhail Trofimov |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Programming |
Tags | Data Science Data Analysis Machine Learning |
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