This repository is for use with the Pearson Publishing (Safari) live webinars:

• "Beginner Machine Learning with scikit-learn."
• "Intermediate Machine Learning with scikit-learn."
• "Advances Machine Learning with scikit-learn."

Versions of this material are used by other training provided by David Mertz and KDM Training.

If you have attended one of the webinars using this material, I encourage you to complete the survey on it at: Machine Learning with scikit-learn survey. As folks fill this out, we will fold back the updated answers into the dataset used in the lessons themselves.

Before attending this course, please configure the environments you will need. Within the repository, find the file requirements.txt to install software using pip, or the file environment.yml to install software using conda. I.e.:

$ conda env create -f environment.yml
$ conda activate Pearson-ML
(Pearson-ML) $ jupyter notebook Outline.ipynb

Or

$ pip install -r requirements.txt
$ jupyter notebook Outline.ipynb

• Machine Learning with scikit-learn

• (Video) Machine Learning with scikit-learn LiveLessons, by David Mertz

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron

• Deep Learning with Python, by Francois Chollet

• Introduction to Machine Learning with Python: A Guide for Data Scientists, by by Andreas C. Müller & Sarah Guido

• Python Data Science Handbook: Essential Tools for Working with Data, by Jake VanderPlas

• Documentation of scikit-learn