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![The Kaggle Book: Data analysis and machine learning for competitive data science by [Konrad Banachewicz, Luca Massaron, Anthony Goldbloom]](https://m.media-amazon.com/images/I/41w7uhlxSDL._SX260_.jpg)
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From the Publisher
Opening words from Anthony Goldbloom, Kaggle founder & CEO
I had a background in econometrics but became interested in machine learning techniques. As I started discovering my interest, I found the field intimidating to enter. It was always my dream that Kaggle would allow people like me the opportunity to break into this powerful new field. Perhaps the thing I’m proudest of is the extent to which Kaggle has made data science and machine learning more accessible.
Luca and Konrad’s book helps make Kaggle even more accessible. It offers a guide to both how Kaggle works, as well as many of the key learnings that they have taken out of their time on the site. Collectively, they’ve been members of Kaggle for over 20 years, entered 330 competitions, made over 2,000 posts to Kaggle forums, and shared over 100 notebooks and 50 datasets. They are both top-ranked users and well-respected members of the Kaggle community.
Key Features:
- All the essential Kaggle knowledge assembled in one place
- Filled with competition analysis, sample code, and end-to-end pipelines for data analysis
- Learn how to use Kaggle to build a data science portfolio and network with the community
Why did you write this book and which is your favorite part?
You can find lots of information on Kaggle about competing, but it is difficult to know what is relevant and also very expensive in terms of time and effort – so we put all the essential knowledge into one book.
Konrad: My favorite part is Chapter 12 on simulation competitions. Reinforcement learning is a field I have been getting into over the last few years – unlike computer vision or NLP, it has yet to reach wider appeal outside academic circles. It was an interesting and educational experience to try and distill what I have learned into a useful introduction to that fascinating domain.
Luca: I enjoyed writing about the history of Kaggle and the professional opportunities it offers. By analyzing how Kaggle has been designed, built, maintained, and developed, as well as studying its forerunners and competitors, I got more clear insights into how it works and why it is important for data scientists – aspects I really couldn't get from just participating in competitions.
Is Kaggle relevant to real-world data science?
Luca: Yes. On Kaggle, you can see what is and isn't possible, given actual technology and practices. Some solutions to Kaggle competitions are the real state of the art in our profession. From an empirical point of view, Kaggle provides you the skills and knowledge to be a better practitioner. There is no other place in the world where you can find so much data and so many problems to solve.
Konrad: It most certainly is. Firstly, whether you are a beginner or a veteran in data science, you can always find new ways to learn, from introductory courses to competitions where SOTA libraries get crash-tested. Secondly, the culture of sharing; I struggle to think of another environment where you can learn from world-class experts demonstrating how they actually do things – for free! Lastly, participating in competitions teaches you how to build a complete data science solution, from initial data analysis and simple benchmarks, through to validating as sophisticated a model as you desire.
Topics Covered:
- Competition Tasks and Metrics
- Designing Good Validation
- Hyperparameter Optimization
- Ensembling with Blending and Stacking Solutions
- Modeling for NLP
- Modeling for Tabular Competitions
- Modeling for Computer Vision
- ...and more!
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|---|---|---|
| The Kaggle Book | The Kaggle Workbook | |
| Book Aims | Structured learning on the techniques to succeed in Kaggle competitions and data science projects | Practical walkthroughs of a selection of competitions to show you how to create effective solutions |
| After Reading the Book | You'll have a better understanding of data science and machine learning for building your career | You'll have a deeper knowledge of how to improve your models and move up the Kaggle leaderboard |
| Topics | Data, metrics, validation, optimization, machine learning, and creating data science portfolios | Time series, vision transformers, natural language processing, gradient boosting, and autoencoders |
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John
Excellent quality
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A black and white book for a 2.3k
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Kaggle for Newbies and Pros
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