TensorFlow: Simple Regression and Classification Models from Skillsoft

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

Learning 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