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Beginning Supervised Machine Learning with Python

Classification and regression

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Topic: Data
Matt Harrison

It's common knowledge that when undertaking a machine learning project, most of your time is spent preparing and tweaking your data so that the libraries and algorithms will work on it. But many don't know that you can take advantage of Python's optimized libraries to run your algorithms more quickly.

Join expert Matt Harrison for an overview of classification and regression tasks with Python using Jupyter and pandas—the same tools used throughout industry to prepare data for analysis. Matt walks you through common machine learning actions, including regression analysis and how to predict continuous variables, how to label data given a labeled training set, and model evaluation and tuning, providing you the valuable hands-on experience you need to get started using them in your own work.

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

By the end of this live online course, you’ll understand:

  • Basic machine learning tasks
  • How to use Python and Jupyter to perform machine learning

And you’ll be able to:

  • Use pandas to load and preprocess data
  • Run regressions, classifications, and other common machine learning tasks

This training course is for you because...

  • You're a programmer who wants to learn how to use Python for machine learning tasks for classification or regression.
  • You're a data scientist with experience in SAS or R and would like an introduction to the Python ecosystem.


  • Programming experience in any language
  • Familiarity with Python (useful but not required)

Recommended follow-up:

About your instructor

  • Matt runs MetaSnake, a Python and Data Science training and consulting company. He has over 15 years of experience using Python across a breadth of domains: Data Science, BI, Storage, Testing and Automation, Open Source Stack Management, and Search.


The timeframes are only estimates and may vary according to how the class is progressing

Introduction to Jupyter (25 minutes)

  • Presentation: Exploring the functionality you’ll need to be successful with Jupyter
  • Q&A

Common data cleaning operations (25 minutes)

  • Presentation: Data preparation before running machine learning algorithms
  • Q&A
  • Break (5 minutes)

Regression: Predicting a continuous value (30 minutes)

  • Presentation: Leveraging the scikit-learn library to create regression models
  • Jupyter Notebook exercise: Create a regression model
  • Q&A

Regression evaluation (30 minutes)

  • Presentation: Using the Yellowbrick library to see how a regression model performs
  • Jupyter Notebook exercise: Evaluate your regression model
  • Q&A
  • Break (5 minutes)

Classification: Assigning a category (25 minutes)

  • Presentation: Using scikit-learn to explore creating classification models
  • Jupyter Notebook exercise: Create a classification model
  • Q&A

Classification evaluation (25 minutes)

  • Presentation: Using Yellowbrick to visualize classification models to understand model performance
  • Jupyter Notebook exercise: Evaluate your classification model
  • Q&A

Wrap-up and Q&A (10 minutes)