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Course Description

TH4P Hands-on: Applied Machine Learning in Python

November 18, 2021

12:15 pm - 3:30 pm

Duration: Afternoon Class

Level: Beginner to Intermediate

Prerequisite: None

Deanne Larson, Ph.D.

DM, CBIP

President

Larson & Associates

Python is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using Python. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be leveraged in building Python machine learning skills.

Attend all three days of the bootcamp to earn a TDWI certificate.

You Will Learn

  • How to use linear models such as linear regression and logistic regression
  • How to choose features and complete feature reduction
  • How to use clustering models such as k-means
  • How to do time series forecasting
  • How to use classification models such as decision trees and k-nearest neighbor
  • How to use ensemble methods
  • How to do model validation

Geared To

  • Those that have foundational knowledge of Python and machine learning and are looking to expand their knowledge and apply best practices

Hands-on Information

In this session, the instructor will teach you principles and practices, show you how to use the tools, and demonstrate with live examples. You will receive installation instructions and take-home workshop materials to complete hands-on exercises on your own, after the live session.

Completion of take-home workshop exercises will require Python and several open-source libraries. Detailed installation instructions will be provided during class.

TDWI LIVE STUDIO AUDIENCE:
This session will be recorded for development of an online learning course which will subsequently be available for purchase directly or via subscription. By attending, you agree that your likeness may appear in the online course, including audio and video.

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