TensorFlow: Simple Regression and Classification Models

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description

Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.

Expected Duration (hours)
1.6

Lesson Objectives

TensorFlow: Simple Regression and Classification Models

Course Overview

recognize linear regression problems and extend that to general machine learning problems

recognize how model parameter training happens via gradient descent to find minimum loss

load a dataset and explore its features and labels

choose the right form of data to feed into the linear regression model

build a base model for comparison with scikit-learn

create placeholders, training variables, and instantiate optimizers to use with regression

train model parameters using a session and the training dataset, and visualize the result with Matplotlib

demonstrate how to interpret the loss and summaries on TensorBoard

choose the high-level Estimator API for common use cases

train a regression model using the high-level Estimator API

evaluate and predict housing prices using estimators

identify classification problems and recall logistic regression for classification

recognize cross entropy as the loss function for classification problems and use softmax for n-category classification

identify data as being a continuous range or comprised of categorical values

work with training and test data to predict heart disease

train the high-level estimator for classification and use it for prediction

describe basic concepts of the linear regression machine learning model

describe basic concepts of the binary classification machine learning model

Course Number:
it_sdaidt_02_enus

Expertise Level
Intermediate