Cloud Platform 102 – Google Machine Learning with Cloud ML

Class Description
This 1 day instructor led course builds upon Cloud Platform 100A and Cloud Platform CPB 101 (which are prerequisites). Through a combination of instructor-led presentations, demonstrations, and hands-on labs, participants will deepen their familiarity with Machine Learning and Tensorflow concepts, whilst gaining hands-on skills in developing, evaluating, and productionizing machine learning models.

Course Benefits

  • Participants will be definitively positioned on the cusp of workplace technology innovation, and predictive analytics.
  • For those participants adequately qualified, this class represents an avenue to facilitate one’s own tangible improvements in workflow time, cost, and effort.
  • Participants will be afforded the opportunity to become proficient at combining features and functions; building scalable and deployable models; and building one’s own Machine Learning Model

Who Should Attend
This class is intended for programmers and data scientists responsible for developing predictive analytics using machine learning. The typical audience member has experience analyzing and visualizing big data, implementing cloud-based big data solutions, and transforming/processing datasets.

Suggested Prerequisites

  • Python and familiarity with the numpy package
  • Undergraduate-level statistics to the level of Udacity ST101

Course Outline
By the end of this course, Students should know how to:

  • Understand what kinds of problems machine learning can address
  • Build a machine learning model using TensorFlow
  • Build scalable, deployable ML models using Cloud ML
  • Know the importance of preprocessing and combining features
  • Incorporate advanced ML concepts into their models
  • Employ ML APIs
  • Productionize trained ML model

Related Classes

Our goal is to make sure your class meets your objectives, not ours. Therefore, all of our outlines are treated as guides to help steer the workshop. This outline does not guarantee that all the topics listed will be covered in the time allowed. The amount of material covered is based on the skill level of the student audience. We may change or alter course topics to best suit the classroom situation.