Building Classification Models with TensorFlow

This course covers the finer points of building such models as well the logistic regression, nearest-neighbor methods, and metrics for evaluating classifiers such as accuracy, precision, and recall.
Course info
Rating
(33)
Level
Intermediate
Updated
Oct 19, 2017
Duration
3h 15m
Table of contents
Course Overview
Overview of Classification Models
Simple Classification Models in TensorFlow
Convolutional Neural Networks for Classification in TensorFlow
Recurrent Neural Networks for Classification in TensorFlow
Description
Course info
Rating
(33)
Level
Intermediate
Updated
Oct 19, 2017
Duration
3h 15m
Description

TensorFlow is a great way to implement powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. In this course, Building Classification Models with TensorFlow, you'll learn a variety of different machine learning techniques to build classification models. First, you'll begin by covering metrics, such as accuracy, precision, and recall that can be used to evaluate classification models and determine which metric is the right one for your use case. Next, you'll delve into more traditional machine learning techniques such as logistic regression and the k-nearest neighbor methods for classification. Finally, you'll discover how to implement more powerful classification models such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, you'll have a better understanding of how to build classification models with TensorFlow.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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