Leverage the power of Generative AI with Kaggle Models
Purchase of the print or Kindle book includes a free PDF eBook
Key Features:
Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models
Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound
Improve the style and readability of your Notebooks, making them more impactful and compelling
Book Description:
Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this field and rise to the top in the Kaggle Notebooks tier. The book is structured as a seven-step data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.
For each topic, we provide one or more Notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature introduced progressively, initially as part of a Notebook, and later extracted for use across future Notebooks to enhance code reusability on Kaggle. It aims to make the Notebooks' code more structured, easy to maintain, and readable.
Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualification to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refine your model and prepare for a submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.
What You Will Learn:
Approach a dataset or competition to perform data analysis via a notebook
Learn data ingestion and address issues arising with the ingested data
Structure your code using reusable components
Analyze in-depth both small and large datasets of various types
Distinguish yourself from the crowd with the content of your analysis
Enhance your notebook style with a color scheme and other visual effects
Captivate your audience with data and compelling storytelling techniques
Who this book is for:
This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:
Beginners on Kaggle from any background
Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization
Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings
Professionals who already use Kaggle for learning and competing