Getting Started with Jupyter Notebook you own this product

intermediate Python • intermediate Python standard library • basics of scikit-learn • basics of classification • basics of evaluating classifiers
skills learned
collaborate on Jupyter Notebook projects • customize your notebooks • work with Jupyter Notebook extensions
Orsolya Putz and Zoltan Varju
1 week · 4-6 hours per week · BEGINNER

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Look inside
In this liveProject, you’ll get hands-on experience using Jupyter Notebook in a real-world data science project. You’ll train a simple KNN classifier and use Jupyter, IPython, and the easy-to-use Markdown markup language to document and share your work. Your challenges will include customizing your notebooks, incorporating your notebooks into a data science project, and sharing your projects with the community on GitHub.

project authors

Orsolya Putz
Orsolya Putz holds a PhD in Cognitive Linguistics and devotes herself to cognitive sciences. Currently, she is an assistant lecturer at Eötvös Loránd University, Budapest, and the co-founder of Crow Intelligence, a boutique consultancy specializing in NLP and AI. Her main research areas are cognitive metaphor theory, text analytics, and cognitive background of biases in human and machine models. She also worked as a linguistic expert on various text analytics projects.
Zoltan Varju
Zoltán Varjú has been working as an expert in Natural Language Processing for 15 years. He was the head of several text analytics and enterprise search projects in the financial and health sectors. Having led Data Science and NLP teams at small companies, large corporations, and NGOs, now he is building his own enterprise, called Crow Intelligence, a boutique consultancy specialized in NLP and AI. He is the founder of the Hungarian Natural Language Processing Meetup.


This liveProject is for intermediate Python programmers at the start of their data science careers. Some experience with virtual environments and integrated development environments, as well as a markup language, will be useful but not essential. To begin this liveProject you will need to be familiar with:

  • Intermediate Python
  • Basics of scikit-learn
  • Basics of classification
  • Basics of evaluating classifiers

you will learn

In this liveProject, you’ll master handy tools for documenting and sharing Python data science projects.

  • Document work with Markdown
  • Collaborate on Jupyter Notebook projects
  • Render math formulas with embedded LaTeX
  • Customize your notebooks
  • Use Jupyter Notebook extensions
  • Share work on GitHub


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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