Machine Learning on Google Cloud Platform

Name: Machine Learning on Google Cloud Platform
Price: 29950.00 ZAR

What is Machine Learning, and what problems can it solve? This five-day course provides a deep understanding of Machine Le

5 day course

Supporting material

Virtual, Private

Virtual Classroom

A convenient and interactive learning experience, that enables you to attend one 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 will run over five consecutive days and is offered as a Virtual Classroom course, which will be hosted from the UK. It is also available as a private training session and can be delivered at our own training venues in the Rosebank Link, Johannesburg or Umhlanga, or any location of your choice.

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

Upcoming courses

Virtual Classroom

Machine Learning on Google Cloud Platform

Mon, Jun 07 2021

R29,950 ex VAT

ADD TO BASKET

Upcoming courses

Virtual Classroom

Machine Learning on Google Cloud Platform

Mon, Jun 07 2021

R29,950 ex VAT

ADD TO BASKET

Book this course