TDWI Boston Seminar

Applied Machine Learning in Python NEW!

May 15, 2019

Duration: Full Day Course

Prerequisite: R programming knowledge, statistics, and exposure to the machine learning process/algorithms

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.

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 do to 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

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TDWI Boston Seminar

MicroTek Training Center 25 Burlington Mall Road
Suite 204
Burlington, MA 01803
May 13–15

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