Data Analysis and Machine Learning with Kaggle: How to win competitions on Kaggle and build a successful career in data science by

Data Analysis and Machine Learning with Kaggle: How to win competitions on Kaggle and build a successful career in data science

Page: 384
Format: pdf, ePub, mobi, fb2
ISBN: 9781801817479
Publisher: Packt Publishing
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Free audiobook downloads for kindle Data Analysis and Machine Learning with Kaggle: How to win competitions on Kaggle and build a successful career in data science English version 9781801817479 PDF MOBI

Get a step ahead of your competitors with a concise collection of smart data handling and modeling techniques Learn how Kaggle works and how to make the most of competitions from two expert Kagglers Sharpen your modeling skills with ensembling, feature engineering, adversarial validation, AutoML, transfer learning, and techniques for parameter tuning Discover tips, tricks, and best practices for winning on Kaggle and becoming a better data scientist Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with the rest of the community, and gain valuable experience to help grow your career. The first book of its kind, Data Analysis and Machine Learning with Kaggle assembles the techniques and skills you'll need for success in competitions, data science projects, and beyond. Two masters of Kaggle walk you through modeling strategies you won't easily find elsewhere, and the tacit knowledge they've accumulated along the way. As well as Kaggle-specific tips, you'll learn more general techniques for approaching tasks based on image data, tabular data, textual data, and reinforcement learning. You'll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Get acquainted with Kaggle and other competition platforms Make the most of Kaggle Notebooks, Datasets, and Discussion forums Understand different modeling tasks including binary and multi-class classification, object detection, NLP (Natural Language Processing), and time series Design good validation schemes, learning about k-fold, probabilistic, and adversarial validation Get to grips with evaluation metrics including MSE and its variants, precision and recall, IoU, mean average precision at k, as well as never-before-seen metrics Handle simulation and optimization competitions on Kaggle Create a portfolio of projects and ideas to get further in your career This book is suitable for Kaggle users and data analysts/scientists of all experience levels who are trying to do better in Kaggle competitions and secure jobs with tech giants. Introducing Data Science competitions Organizing Data with Datasets Working and learning with kaggle notebooks Leveraging Discussion forums Detailing competition tasks and metrics Designing good validation schemes Ensembling and stacking solutions Modelling for tabular competitions Modeling for image classification and segmentation Modeling for Natural Language Processing Handling simulation and optimization competitions Creating your portfolio of projects and ideas Finding new professional opportunities

Do people still use Kaggle for Data Science competitions? I'm
        Kaggle nowadays is a great way for a company/institution/organization to spend machine learning to 12 in Kaggle rankings and winning two competitions 6 answers  ·  1 vote: The following is only my own opinion. Others may disagree.Kaggle nowadays is a great way
        How can I increase my 'employability' with Kaggle?
        I can advertise good programming skills, it's more difficult for me to show up some I wanted to actually start doing data science and machine learning 
        How similar are Kaggle competitions to what data scientists do?
        That alone already puts it in the minority of real world data science problems. You are not required to 'explain how this machine learning thing works' to a non 11 answers  ·  434 votes: There are two key differences that I want to elucidate.Kaggle competitions optimizes
        Is winning a Kaggle competition prestigious? - Quora
        Yes, on average there are almost 2000 people/teams participate in a competition, all are good in machine learning, data science, and analytics. It's no less 11 answers  ·  1,200 votes: Look, I’ve won 2 Kaggle competitions, including a prestigious Masters only competition,
        What important skills can we learn by participating in  - Kaggle
        In order to win most of the competitions, you have to do this. Analysis skills - One of the things that distinguishes a good data scientist from an

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