Machine Learning on Google Cloud

What is Machine Learning, and what problems can it solve? This five-day course provides a deep understanding of Machine Learning, its implementation and the value it can provide.
google badge
5 day course
Supporting material
Google Cloud Partner of the Year
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

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 offered as a private training session that can be run at a location of your choice or virtually.

Course overview
Who should attend:

This course is designed for Data Engineers and Programmers interested in learning how to apply machine learning in practice and anyone interested in learning how to leverage machine learning in their enterprise.

Walk away with the ability to:
  • Frame a business use case as a machine learning problem
  • Describe how to improve data quality
  • Perform exploratory data analysis
  • Build and train supervised learning models
  • Optimise and evaluate models using loss functions and performance metrics
  • Create repeatable and scalable training, evaluation, and test datasets
  • Implement machine learning models using Keras and TensorFlow 2.x
  • Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalisation
  • Represent and transform features
  • Train models at scale with AI platform

Foundational skills in Google Cloud and Data which can be derived from our Google Cloud Fundamentals: Big Data & Machine Learning course.

Course agenda
Module 1: How Google does Machine Learning
  • Develop a data strategy around machine learning
  • Examine use cases that are then reimagined through an ML lens
  • Recognise biases that ML can amplify
  • Leverage Google Cloud 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
Module 2: Launching into Machine Learning
  • Describe how to improve data quality
  • Perform exploratory data analysis
  • Build and train supervised learning models
  • Optimise and evaluate models using loss functions and performance metrics
  • Mitigate common problems that arise in machine learning
  • Create repeateable and scalable training, evaluation and test datasets
Module 3: Introduction to TensorFlow
  • Create TensorFlow 2.x and Keras machine learning models
  • Describe Tensorflow 2.x key components
  • Use the library to manipulate data and large datasets
  • Use the Keras Sequential and Functional APIs for simple and advanced model creation
  • Train, deploy, and productionalise ML models at scale with Cloud ML Platform
Module 4: Feature Engineering
  • Compare the key required aspects of a good feature
  • Combine and create new feature combinations through feature crosses
  • Perform feature engineering using BQML, Keras, and TensorFlow 2.x
  • Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep
  • Understand and apply how TensorFlow transforms features
Module 5: The Art and Science of ML
  • Optimise model performance with hyperparameter tuning
  • Experiment with neural networks and fine-tune performance
  • Enhance ML model features with embedding layers
Book this course
Call our sales team today
Don't miss out
Keep up to date with news, views and offers from Jellyfish Training.
Your data will be handled in accordance with our Privacy Policy