Google Cloud Machine Learning Training Overview
This Machine Learning with TensorFlow on Google Cloud training course teaches attendees how to write distributed machine learning (ML) models that scale in TensorFlow. Students learn how to incorporate the right mix of parameters to yield accurate, generalized models to solve specific types of ML problems.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, we offer some courses as live, instructor-led online classes for individuals.
Objectives
- Frame a business use case as a machine learning problem
- Create machine learning datasets that are capable of achieving generalization
- Implement machine learning models using TensorFlow
- Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
- Build and operationalize distributed TensorFlow models
- Represent and transform features
Prerequisites
All students should have:
- Experience coding in Python
- Knowledge of basic statistics
- Knowledge of SQL and cloud computing (helpful)
Outline
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Introduction
How Google Does Machine Learning
- Develop a data strategy around machine learning
- Examine use cases that are then reimagined through an ML lens
- Recognize biases that ML can amplify
- Leverage Google Cloud Platform tools and environment to do ML
- Learn from Google's experience to avoid common pitfalls
- Carry out data science tasks in online collaborative notebooks
- Invoke pre-trained ML models from Cloud Datalab
Launching into Machine Learning
- Identify why deep learning is currently popular
- Optimize and evaluate models using loss functions and performance metrics
- Mitigate common problems that arise in machine learning
- Create repeatable and scalable training, evaluation, and test datasets
Intro to TensorFlow
- Create machine learning models in TensorFlow
- Use the TensorFlow libraries to solve numerical problems
- Troubleshoot and debug common TensorFlow code pitfalls
- Use tf_estimator to create, train, and evaluate an ML model
- Train, deploy, and productionalize ML models at scale with Cloud ML Engine
Feature Engineering
- Turn raw data into feature vectors
- Preprocess and create new feature pipelines with Cloud Dataflow
- Create and implement feature crosses and assess their impact
- Write TensorFlow Transform code for feature engineering
The Art and Science of ML
- Optimize model performance with hyperparameter tuning
- Experiment with neural networks and fine-tune performance
- Enhance ML model features with embedding layers
- Create reusable custom model code with the Custom Estimator
Conclusion
Training Materials
All GCP training students receive comprehensive courseware.
Software Requirements
Students must have a modern web browser (ideally Chrome) and Internet access.
