Course Description

This free online course in Tensor Flow machine learning models will begin by introducing you to the mathematical aspects of deep learning. You will also be introduced to the tensors encountered in machine learning practice, the key tensor operations in deep learning and the basics of training and regularization in deep learning. This course explains how to build input pipelines for TensorFlow and the various methods of creating datasets.

The course then introduces the process for text processing with Tensorflow. You will also learn about the concept of tokenization, the function of tokenizers, embedding, text operations and the conversion of strings. Next, you will learn how to load text into datasets, encode datasets into numbers, and how to build a vocabulary set.

The course then explains how to build a neural network model for an image classification task using Tensorflow Keras API. You will also learn about the feed-forward neural network and the fashion MNIST data set. This course analyzes the process and significance of building models for structured data using TensorFlow API. You will also learn about the types of feature columns and demonstrate how to transform a column from the data frame.

What you'll learn in this course?

• Technology

• Machine Learning

• Deep Learning

• TensorFlow