• Online, Self-Paced
Course Description

Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.

Learning Objectives

TensorFlow: Introduction to Machine Learning

  • Course Overview
  • describe kinds of machine learning algorithms and their use cases
  • define the training and prediction phases in machine learning
  • define the conceptual differences between traditional machine learning and deep learning
  • compare and contrast supervised and unsupervised techniques in machine learning
  • define the advantages and challenges in using TensorFlow for machine learning
  • distinguish data and computations as distinct building blocks of a TensorFlow computation graph
  • choose the right way to install TensorFlow based on the user's environment
  • install TensorFlow and work with Jupyter Notebooks
  • specify constants and build and run a computation graph
  • use TensorBoard to visualize the computation graph
  • build and execute a computation graph with variables and placeholders
  • visualize variables and placeholders on TensorBoard
  • recognize how variables are trainable parameters and can be updated within a session
  • work with feed dictionaries to input data to placeholders during training
  • use named scopes to group computations
  • specify and work with eager execution for prototyping and development
  • recall basic concepts of machine learning and TensorFlow
  • build and execute computation graphs with computation nodes and data

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.