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Machine Learning on Google Cloud Platform

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.
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5 day course
Supporting material
Virtual, Private
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.
Private
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 five-day course you will start to think strategically and analytically about Machine Learning as a business process and consider the implications of starting to use Machine Learning models.

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 optimisation 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, generalizable solution using gradient descent and creating datasets.

Our Machine Learning on Google Cloud Platform course is available as a private training session and will run over five consecutive days. It can be delivered at our own training venue in the World Trade Center, Barcelona, a location of your choice or via Virtual Classroom.

 
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:
  • Think strategically and analytically about ML as a business process
  • Consider the fairness implications with respect to ML
  • Optimisation how ML works and how various hyperparameters affect models during optimisation
  • Write models in TensorFlow using both custom and pre-made estimators
  • Train ML models locally or in Cloud AI Platform
  • Use various technologies including Cloud Dataflow and Cloud Dataprep
Prerequisites

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

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 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
Module 2: Launching into Machine Learning
  • Identify why deep learning is currently popular
  • Optimise 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
Module 3: Introduction 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 productionalise ML models at scale with Cloud ML Engine
Module 4: 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
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
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