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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- 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
- Anaconda
- Colab
- Google AI Platform
- AWS SageMaker
- Azure ML Studio
- What is Keras?
- Why Keras?
- Keras Basics
- Doing Deep Learning using Keras
- Hands-on Exercises
- What is Feature Engineering?
- Why Feature Engineering?
- How to apply Feature Engineering?
- Discussions on various scenarios
- Hands-on Exercises
- Development Process
- Key Tuning Strategies
- Preparing, Versioning, and Testing for Deployment
- Hands-on Exercises
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- Python Basics
