5 day course 
Supporting material 
Private Course Credits
Select the pre-paid training investment that’s right for you and help your money stretch a little further with our course credits.
Jellyfish is an award-winning Google Cloud Partner. Our trainers work with Google Cloud on a daily basis, so you'll benefit from the years of industry experience they’ll share with you.
The session focuses on teaching you to implement the various flavors of production ML systems and solve an ML problem by building an end-to-end pipeline; going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving.
You'll develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning while forecasting time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs.
By the end of the session, you'll also be able to implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow.
This Advanced Machine Learning with TensorFlow on Google Cloud course is available as a private session that can be delivered virtually or at a location of your choice in the US.
Course overview
Who should attend:
This course is ideal for data engineers and programmers interested in learning how to apply machine learning in practice. It's also perfect for anyone interested in learning how to build and operationalize TensorFlow models.
What you'll learn:
By the end of this course, you will be able to:
- Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing
- Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving
- Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning
- Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs. • Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models
- Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow
Prerequisites
In order to get the most out of this session, you should already have knowledge of machine learning and TensorFlow equivalent to the level of Machine Learning on GCP specialization. Prior experience with programming languages such as SQL and Python, as well as cloud computing, will also be helpful.
Course agenda
- In the first module, we recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization
- Compare static vs. dynamic training and inference
- Manage model dependencies
- Set up distributed training for fault tolerance, replication, and more
- Export models for portability
- Classify images using deep learning
- Implement convolutional neural networks
- Improve the model by augmentation, batch normalization, etc.
- Leverage transfer learning
- Predict future values of a time-series
- Classify free form text
- Address time-series and text problems with recurrent neural networks
- Choose between RNNs/LSTMs and simpler models
- Train and reuse word embeddings in text problems
- Devise a content-based recommendation engine
- Implement a collaborative filtering recommendation engine
- Build a hybrid recommendation engine with user and content embeddings