Machine Learning Mastery Workshop

Machine Learning Mastery Workshop

5 Day Course

Online - Live Virtual

Course Details

Course Details

This 22.5 hour class focuses on essential materials on machine learning and data visualization. It provides the skills needed by scientists, engineers, data scientists, data analysts, and business intelligence experts to use Python and machine learning for their data mining, classification, and predictive modeling tasks. This highly interactive training will empower your team with the skills they need to build reliable, repeatable analyses, and prediction workflows. After this class, they will be able to significantly increase the amount of data they can process, thanks to automation, and speed up the classification, interpretation, and analysis of data.

This course is instructor-led. Consult the class schedule for times and locations. Course registration will close at 12pm CT the Thursday before a Monday start date.

Course Overview

Artificial intelligence and machine learning are defining features of the 21st century and are quickly becoming a key factor in gaining and maintaining competitive advantage in each industry which incorporates them.

In this course, we combine conceptual knowledge of machine learning with extensive experience applying it to real-world data. Your team will develop skills in applying Python’s machine learning tools, such as the scikit-learn package, to make predictions about complicated phenomena by leveraging the information contained in numerical data, natural language, images, and discrete categories.

The emphasis is on learning techniques to maximize the predictive performance of machine learning workflows focusing on the different types of feature sources for machine learning. For each, we progress through a short introductory lecture followed by exercises of progressive difficulty. Intermingled with the machine learning material are short discussions of helpful and diagnostic data visualizations.

  • Use specific regression, classification, and clustering algorithms skillfully to model data and solve problems by leveraging the full power of the scikit-learn API
  • Engineer numeric features to maximize predictive power
  • Visualize interactions and non-linear distributions of data using matplotlib and seaborn
  • Validate models with the appropriate success metrics
  • Troubleshoot common issues like unbalanced labels and high dimensionality data
  • Build deep insight by retrieving model parameters
Syllabus