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Hands-on Machine Learning with Python: Classification and Regression

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Matt Harrison

This course will give you an overview of machine learning with Python. You will see and use the same tools that industry uses. We will be using Jupyter and pandas to prepare data to analyze. We will look at common machine learning actions-regression, and classification.

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

Participants will 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 are a programmer and would like to see how to use Python for machine learning tasks for classification or regression.
  • You are a data scientist with experience in SAS or R and would like an introduction to the Python ecosystem


  • A url with a Jupyter notebook will be distributed to students prior to the class
  • Familiarity with the Python programming language is useful, though if you have programming experience, you should be able to get through the course

Please come prepared with a clear schedule so you can participate in the hands-on portions. Rather than just listening to the instructor drone on, you will get the chance to try your hand at machine learning.

Materials or Downloads Needed in Advance:

Install Anaconda and Jupyter to try out on your own time

Recommended preparation:

Introduction to Pandas for Developers (video)

Chapters 2, and 5-10 of Python for Data Analysis (book)

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 - 20 min

  • Common Data Cleaning Operations - 20 min

  • Regression - Predicting a continuous value - 40 min

Break - 10 min

  • Regression Evaluation - 30 min

  • Classification - Assigning a category - 30 min

  • Classification Evaluation - 30 min