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Developing Kaggle Notebooks

Developing Kaggle Notebooks

By : Gabriel Preda
5 (29)
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Developing Kaggle Notebooks

Developing Kaggle Notebooks

5 (29)
By: Gabriel Preda

Overview of this book

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep 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 qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.
Table of Contents (14 chapters)
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12
Other Books You May Enjoy
13
Index

Kaggle Learn

Kaggle Learn is one of the lesser-known gems on Kaggle. It contains compact learning modules, each centered on a certain subject related to data science or machine learning. Each learning module has several lessons, each one with a Tutorial section followed by an Exercise section. The Tutorial and Exercise sections are available in the form of interactive Kaggle Notebooks. To complete a learning module, you need to go through all the lessons. In each lesson, you will need to review the training material and successfully run the Exercise Notebook. Some of the cells in the Exercise Notebook have a verification associated with them. If you need help, there are also special cells in the notebook that reveal hints about how to solve the current exercise. Upon completing the entire learning module, you receive a certificate of completion from Kaggle.

Currently, Kaggle Learn is organized into three main sections:

  • Your Courses, where you have the courses that you have completed and those that are now in progress (active).
  • Open courses that you can explore further. The courses in this main section are from absolute beginner courses (such as Intro to Programming, Python, Pandas, Intro to SQL, and Intro to Machine Learning) to intermediate courses (such as Data Cleaning, Intermediate Machine Learning, Feature Engineering, and Advanced SQL). Also, it contains topic-specific courses like Visualization, Geospatial Analysis, Computer Vision, Time Series, and Intro to Game AI and Reinforcement Learning. Some courses touch on extremely interesting topics such as AI ethics and machine learning interpretability.
  • Guides, which is dedicated to various learning guides for programs, frameworks, or domains of interest. This includes the JAX Guide, TensorFlow Guide, Transfer Learning for Computer Vision Guide, Kaggle Competitions Guide, Natural Language Processing Guide, and R Guide.

Kaggle is also committed to supporting continuous learning and helping anyone benefit from the knowledge accumulated on the Kaggle platform and the Kaggle community. In the last two years, Kaggle has started to reach out and help professionals from underrepresented communities acquire skills and experience in data science and machine learning in the form of the KaggleX BIPOC (Black, Indigenous, and People of Color) Grant program, by pairing Kagglers, as mentors, with professionals from BIPOC communities, as mentees.

In the next section, we will familiarize ourselves with a rapidly evolving capability of the Kaggle platform: Models.

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