Machine Learning Engineering Tutorial

An Machine Learning Engineering Tutorial is an engineering tutorial on how to build ML-based applications (with ML techniques and ML tools).

• Context:
  • It can include portions of a Data Science Tutorial and a Data Driven Software Engineering Tutorial.
• Example(s):
  • Machine Learning with scikit-learn Tutorial at SciPy2016.
  • Value Estimation with Python[1]
  • …
• Counter-Example(s):
  • a Machine Learning Course.
  • a Transactional Application Engineering Tutorial (for transactional applications)
• See: Applied Natural Language Processing Tutorial.

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References

2017

• Adam Geitgey. (2017). “Value Estimation (with Python)"

2016a

• "Machine Learning with scikit-learn".
  • QUOTE: This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.
  • Part-1: http://youtube.com/watch?v=OB1reY6IX-o
  • Part-2: http://youtube.com/watch?v=Cte8FYCpylk
  • repo: http://github.com/amueller/scipy-2016-sklearn

2016c

• Jerry Kurata. (2016). “Understanding Machine Learning with Python." Pluralsight Tutorial
  • QUOTE: Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.
  • https://app.pluralsight.com/player?course=python-understanding-machine-learning&author=jerry-kurata&name=python-understanding-machine-learning-m0&clip=0&mode=live

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