louisdorard/ML_Kickstart.md

## ML_Kickstart.md

      
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              ML_Kickstart.md
            
          
    Machine Learning Kickstart — Training Workshop at PAPIs (Boston — 10/23/17)

Summary

Gain the skills to create ML 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.
Registration at papis.io/2017
Learning objectives


Understand the possibilities and limitations of ML, and know how to formulate your own ML problem
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.

Program

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
Possibilities and example use cases
[Hands-on] Formalizing your own ML problem

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 BigML (ML-as-a-Service tool) and scikit-learn (open source ML library)

Evaluation


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 BigML and scikit-learn
[Hands-on] Improving prediction accuracy with ensembles of Decision Trees: Random Forests

Operationalization


[Hands-on] Demo of cloud ML APIs: Indico and BigML
[Hands-on] Deploying your own Python models as APIs with Microsoft Azure ML or Flask (open source library)
Critical overview of open source and cloud ML products and deployment solutions

Conclusions


Key take-aways
Resources to go further

Instructor and testimonials

 Louis Dorard is an independent consultant with 10 years of experience in Machine Learning. He is the author of Bootstrapping Machine Learning, General Chair of PAPIs.io (International Conference on Predictive Applications and APIs), and Adjunct Teaching Fellow at UCL School of Management. He has held workshops at major companies such as Intel, Deloitte, Konica Minolta, Dassault Systems, as well as smaller businesses and growing startups. Louis holds a PhD in Machine Learning from University College London.

“Louis is an excellent teacher, you can feel how knowledgeable and passionate he is. I highly recommend this course!” — Charles Camus, Web Developer at Groupe Express
“Very good workshop to demystify Machine Learning solutions and how to put them in practice.” — Sylvain Centelles, Software Development Manager at Intel
“Everyone in the team was really glad they came — growth project manager, product manager, system architect & developers.” — Nicolas Schwartz, Tech Lead at BlaBlaCar

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.

Logistics

The workshop will start at 9am and end at 5.30pm. More information (venue, set-up, etc.) will be provided upon registration.
Copyright

Louis Dorard © 2017