On this course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
You will learn the key phases of converting a candidate use case to be driven by machine learning and the correct order in which these phases should be undertaken. You will also discover how ML optimization works and how various hyperparameters affect models during optimisation.
You will learn 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, and why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep.
You will also cover how to set up a supervised learning problem and find a good, generalisable solution using gradient descent and creating datasets.
Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed. Our Machine Learning on Google Cloud course is available as a private training session that can be delivered at a location of your choice or virtually.