PAPIs Europe 2018 has ended
PAPIs is the 1st series of international conferences dedicated to real-world ML applications, techniques and tools. After 7 previous events on 4 different continents, PAPIs is returning to Europe on 4-6 April 2018. Join us at the Canary Wharf Tower in London!

  • Training Workshops — 4 April, UCL School of Management (Level 38)
  • Industry and Startups — 5 April, Level 39
  • Industry and Research — 6 April, Level 39

More information about the conference at papis.io/europe-2018
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Wednesday, April 4 • 09:00 - 17:30
Machine Learning Kickstart Workshop

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This is a training workshop taking place before the main conference. It will be given in a classroom of up to 20 persons only, to maximize interaction and so you can ask even more questions than in a conference setting.

Gain the skills to create Machine Learning models and use them in your applications, with open source libraries and cloud platforms. In this workshop, we'll demystify Machine Learning, you'll gain an understanding of its possibilities and limitations, and how to put it to work on real problems. You'll learn to create, evaluate and deploy ML models. We'll adopt a top-down, results-first and experimentation-driven approach.

Learning objectives
  • Understand the possibilities and limitations of ML
  • Understand the main ideas behind the most widely used learning algorithms in the industry: decision trees and random forests
  • Know how to build predictive models from data, analyze their performance and deploy to production with APIs.


Each section contains theory and hands-on demos/exercises (where students can reproduce at the same time on their own laptops).

Introduction to Machine Learning
  • Key ML concepts and terminology
  • Formalizing supervised learning problems: classification and regression
  • Possibilities and example use cases (web applications, mobile, enterprise data science)

Model creation
  • Intuitions behind learning algorithms: Nearest Neighbors and Decision Trees
  • [Hands-on] Introduction to Jupyter notebooks
  • [Hands-on] Creating and interpreting Decision Trees with scikit-learn (open source ML library) and BigML (ML-as-a-Service tool), on classification and regression datasets

  • Performance criteria for ML models and evaluation procedure
  • Aggregate metrics for regression (MAE, MSE, R-squared, MAPE) and classification (accuracy, confusion and cost matrices)
  • [Hands-on] Evaluating models with Python, scikit-learn and BigML on previous datasets
  • [Hands-on] Improving prediction accuracy with ensembles of Decision Trees: Random Forests
  • [Hands-on] Embracing randomness with cross-validation

  • [Hands-on] Why and how to use REST APIs for ML use in production
  • [Hands-on] Deploying your own Python models as APIs with the Flask library
  • [Hands-on] Using your API to fill in missing values in a spreadsheet program

  • Recap of key take-aways
  • Resources to go further

Student requirements

Programming experience and basic knowledge of the Python syntax. Code will be provided for students to replicate what will be shown during hands-on demos. Please consult Codeacademy's Learn Python and Robert Johansson's Introduction to Python programming (in particular the following sections: Python program files, Modules, Assignment, Fundamental types, Control Flow et Functions) to learn or revise Python's basics.
  • Basic maths knowledge (undergraduate level) will be useful to better understand some of the theory behind learning algorithms, but it isn’t a hard requirement.
  • Own laptop to bring for hands-on practical work.

avatar for Sébastien Treguer

Sébastien Treguer

Lead Data Science, Tesobe / La Paillasse
Sebastien graduated from a master's degree in science with a specialization in signal and image processing. He has developed his experience in various fields, including finance, energy, healthcare, biology and neuroscience.His background in signal and image processing gave him the... Read More →

Wednesday April 4, 2018 09:00 - 17:30 BST
Level38 NE Theatre Level38, One Canada Square, London