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Hands-on Machine Learning with Python: Clustering, Dimension Reduction, and Time Series Analysis

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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 use. We will be using Jupyter and pandas to prepare data to analyze. We will look at common machine learning actions: clustering and dimension reduction. We will also use the prophet library to do time series forecasting.

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

This training course is for you because...

  • You are a programmer and would like to see how to use Python for machine learning tasks of clustering, dimension reduction, and time series analysis.
  • You are a data scientist with experience in SAS or R and would like an introduction to the Python ecosystem

Prerequisites

  • 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
  • System Test:

To test whether you will be able to run the jupyter notebooks in your upcoming training, please:

Navigate here: https://attendee-testing.oreilly-jupyterhub.com (This is the link to the test site)

  • Sign in with your Safari credentials
  • Click "start my server"
  • Click on "notebook .ipynb"

  • Run each of the code cells: click the cell then either press Shift+Return or click the triangle in the top menu

  • There may be a few second delay, but you should eventually see the graphs. If you do not, this probably means that your firewall is blocking JupyterHub's websockets. Please turn off your company VPN or speak with your system administrator to allow.

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

Schedule

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

  • Introduction to Jupyter - 20 min

  • Common Data Cleaning Operations - 30 min

Break - 5 min

  • Dimension Reduction - 40 min

Break - 5 min

  • Clustering - 40 min

Break - 5 min

  • Time series forecasting - 40 min