Practical Machine Learning and Deep Learning with TensorFlow

Video Learning Path Overview

A Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.

Machine learning and Deep Learning have been gaining immense traction lately, and TensorFlow is quickly becoming the technology of choice for machine learning and deep learning, because of its ease to develop intelligent machine learning applications and powerful neural networks.

This well thought out Learning Path takes a step by step approach to teach you how to use TensorFlow for performing machine learning and deep learning on a day–to–day basis. Beginning with an introduction to machine learning with TensorFlow, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects.

Once you are comfortable in creating models and neural networks with TensorFlow, you will learn to practically apply them to create your own machine learning systems or to solve common machine learning problems such as inaccuracy or duplication of data.

Moving further, the main challenge with deep learning models and applications is to find the right way to deploy them within the company’s IT infrastructure. This is where the serverless framework comes in. You will learn to use serverless framework for deploying your applications on a cloud computing platform such as AWS Lambda. Going serverless with AWS Lambda enables you to separate your application into many small independent services. Thus, increasing your product flexibility and reducing your costs.