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The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY

In-Person Training
Practical machine learning with the Jupyter Notebook

Christian Moscardi (The Data Incubator)
Tuesday, August 22 & Wednesday, August 23, 9:00am - 5:00pm
Location: Concourse F Level: Intermediate
See pricing & packages
Early Price ends June 30

This course will sell out—sign up today!

Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Wednesday.

Christian Moscardi walks you through developing a machine learning pipeline, from prototyping to production, with the Jupyter platform, exploring data cleaning, feature engineering, model building and evaluation, and deployment in an industry-focused setting. Along the way, you'll learn Jupyter best practices and the Jupyter settings and libraries that enable great visualizations.

What you'll learn, and how you can apply it

  • Learn the fundamentals of an end-to data science workflow in Jupyter, including Jupyter tips, tricks, and best practices
  • Explore modeling techniques and their implementations in Python

This training is for you because...

  • You're a Juypter user who wants to learn the fundamentals of an end-to data science workflow within Juypter.

Prerequisites:

  • Programming experience
  • Basic familiarity with math (e.g., linear algebra)

Hardware and/or installation requirements:

  • A laptop with an up-to-date version of Chrome installed

Christian Moscardi walks you through developing a machine learning pipeline, from prototyping to production, with the Jupyter platform, exploring data cleaning, feature engineering, model building and evaluation, and deployment in an industry-focused setting. Along the way, you’ll learn Jupyter best practices and the Jupyter settings and libraries that enable great visualizations.

Outline

Day 1: Recommendation engine
– Overview of data and its wrangling
– Item-item correlations and finding similar items
– User similarity and predicting user ratings
– Collaborative filtering
– Evaluating model performance

Day 2: Anomaly detection
– Data format and goal
– Limitations of time series data
– Detrending and seasonality
– Windowing and local scores
– Setting thresholds for classification
– Online learning

About your instructor

Photo of Christian Moscardi

Christian Moscardi is director of technology for the Data Incubator. Previously, Christian developed a CMS for food blogs, worked for Google, and researched and taught at Columbia. He organizes with BetaNYC, New York’s civic tech organization, and contributes to various civic data projects. His extracurricular activities include cooking, playing the piano, and exploring New York.

Conference registration

Get the Platinum pass to add this course to your package. Early Price ends June 30.

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