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readme.md

Debugging Data Science Live Training

This repository contains the exercises and data for Debugging Data Science Live Training. This training provides an invaluable, hands-on guide to applying machine learning in the wild. Through an end-to-end data science example, we will walk through the process of defining an appropriate problem, building and evaluating a model, and see how to take its performance to the next level through a variety of more advanced techniques. The focus will be on debugging machine learning problems that arise during the model training process and seeing how to overcome these issues to improve the effectiveness of the model.

And/or please do not hesitate to reach out to me directly via email at jondinu@gmail.com or over twitter @jonathandinu

If you find any errors in the code or materials, please open a Github issue in this repository

Skill Level

Intermediate

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

  • Use scikit-learn to build machine learning models and evaluate them using advanced metrics to diagnose learning problems.
  • Improve the performance of a machine learning model through feature selection, data augmentation, and hyperparameter optimization.
  • Walk through an end-to-end applied machine learning problem applying cost-sensitive learning to optimize “profit.”

This training course is for you because...

  • You have taken an introductory machine learning or data science course but want a “second course” in machine learning to understand how to effectively apply the theory to real world problems and troubleshoot issues that might arise.
  • You are an aspiring data scientist looking to break into the field and need to learn the practical skills necessary for what you will encounter on the job.
  • You are a quantitative researcher interested in applying theory to real projects by taking a computational approach to modeling.
  • You are a software engineer interested in building intelligent applications driven by machine learning.

Prerequisites

  • Experience with an object-oriented programming language, e.g., Python (all code demos during the training will be in Python)
  • Familiarity with the basics of supervised machine learning.
  • A working knowledge of the scientific Python libraries (pandas and scikit-learn) is helpful but not required.

Course Set-up

  1. Download the appropriate Python 3.7 Anaconda Distribution for your operating system: https://www.anaconda.com/distribution/
  2. In a Terminal: git clone https://github.com/hopelessoptimism/debugging-data-science.git
  3. cd debugging-data-science
  4. conda env create -f environment.yml
  5. conda activate debugging-data

Recommended Preparation

Recommended Follow-up

Data

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