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You're reading from  Developing Kaggle Notebooks

Product typeBook
Published inDec 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781805128519
Edition1st Edition
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Author (1)
Gabriel Preda
Gabriel Preda
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Gabriel Preda

Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.
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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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Published in: Dec 2023Publisher: PacktISBN-13: 9781805128519
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Author (1)

author image
Gabriel Preda

Dr. Gabriel Preda is a Principal Data Scientist for Endava, a major software services company. He has worked on projects in various industries, including financial services, banking, portfolio management, telecom, and healthcare, developing machine learning solutions for various business problems, including risk prediction, churn analysis, anomaly detection, task recommendations, and document information extraction. In addition, he is very active in competitive machine learning, currently holding the title of a three-time Kaggle Grandmaster and is well-known for his Kaggle Notebooks.
Read more about Gabriel Preda