Course #: GCP-335
Duration: 5 Days
Price: 3,325.00 USD
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Material: LearnGreen (pdf)
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Course Description
Objectives
- Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML
- How ML optimization works and how various hyperparameters affect models during optimization
- How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform
- Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep
Audience
- Data Engineers and programmers interested in learning how to apply machine learning in practice
- Anyone interested in learning how to leverage machine learning in their enterprise
Prerequisites
- Experience coding in Python
- Knowledge of basic statistics
- Knowledge of SQL and cloud computing (helpful)
Topics
- 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
- 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
- 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
- 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
- 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


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