TensorFlow: Introduction to Machine Learning
Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level
Overview/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.

Expected Duration (hours)
1.7

Lesson Objectives TensorFlow: Introduction to Machine Learning

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

Course Number: it_sdaidt_01_enus

Expertise Level
Intermediate