Machine Learning using TensorFlow v2

Develop and Deploy the Machine Learning models using the TensorFlow Framework on Google Cloud Platform

Duration

3 Days

Level

Intermediate Level

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This 3 day course is designed to give practical exposure to participants about –

How to develop Machine Learning models using Tensorflow Framework
How to deploy the solutions on AI Platform Google Cloud Platform

Initial part of the course provides a high level overview about Machine Learning and it’s Pipelines, key ways to implement Machine Learning projects, Google Cloud AI Platform etc.

Second part of this course is oriented towards providing code walks of Machine Learning Models and deploying the same on Google Cloud Platform.

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Data Engineers
Data Scientists
Machine Learning Engineers

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Applied Machine Learning Overview

Quick introduction to Machine Learning
Ways to perform Machine Learning
Machine Learning Use cases and Demos
Common Concepts and Terms
Typical Machine Learning Pipeline
Challenges in Deployment of Machine Learning Models

Key Environments for Experimentation

Anaconda
Colab
Google AI Platform
AWS SageMaker
Azure ML Studio

AI Platform Overview

What is Keras?
Why Keras?
Keras Basics
Doing Deep Learning using Keras
Hands-on Exercises

Keras API

Feature Engineering

What is Feature Engineering?
Why Feature Engineering?
How to apply Feature Engineering?
Discussions on various scenarios
Hands-on Exercises

End to End Tensorflow Model Development, Tuning and Deployment

Development Process
Key Tuning Strategies
Preparing, Versioning, and Testing for Deployment
Hands-on Exercises

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Python Basics

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Machine Learning using TensorFlow v2

Duration

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