A review of the course Google Cloud Machine Learning with Tensorflow on Udemy by Packt.
The following is an in depth review of the course The Google Cloud Machine Learning with tensorflow course on Udemy.
Being familiar with the market’s most used cloud platforms (Google Cloud Platform, Amazon Web Services, and Microsoft’s Azure) is becoming essential for the workflows of Machine Learning engineers and Data Scientist, as they provide access to services like Big Query or Storage capabilities and hardware systems that would otherwise be unreachable.
This course provides an introduction to Google Cloud Platform’s different services, like Big Query, Cloud Storage, Notebooks or AI platform, while describing some simple applications of Machine Learning using these services with Tensorflow 2.
What is it?
The Google Cloud Machine Learning with tensorflow course on Udemy is a course oriented to getting its pupils familiar with Google Cloud Platforms different services, and mainly those oriented to handling data and building predictive models.
By completing it, you will learn how to build models that fetch data from Big Query, pre-process this data using Tensorflow, and make predictions using this fantastic library created by Google.
You will also learn how to upload files to Google cloud storage and use those files when making predictions, or getting data ready for production.
The course is divided into the following sections:
- A quick start with Google Cloud Platform: A simple introduction to the different services offered on Google Cloud, their possible connections, architecture and philosophy.
- Machine Learning with Tensorflow Fundamentals: Introduction to Tensorflow and Tensorboard for monitoring models, Linear Regression, Logistic Regression, and KNN with Tf.
- Basic model training with Tensorflow 2.0: How to pre-process data with Tensorflow, how to train models using the Tf.Keras API, and how to evaluate these models and export them for production availability.
- Advanced Model training with Tensorflow 2.0: This section teaches how to set up distributed training strategies in Tensorflow and how to monitor the advances in these training processes using Tensorboard.
- Serving predictions with Tensorflow on GCP: Different ways to serve predictions on with Google Cloud Platform and how to do this using Tensorflow Serving.
- Neural Networks: An introduction to neural networks, explaining the main concepts, gradient descent, backpropagation and the overall structure of a vanilla neural network.
Each section is composed of short videos (3-15) minutes to make up for a total of about 1h in each section, and a short test afterwards. This tests are very easy, and in our opinion some coding exercise would have been a great complement, as the short, multiple-answer questions in these tests are no real way to evaluate how well the concepts have been understood.
If these tests have been completed successfully a certificate is granted at the end of the course.
We do miss some content related to BigQuery and other parts of the data Google Cloud Platform ecosystem on this course, however, there are many Udemy Bigquery courses out there that also cover the fundamentals of SQL or how to build Data Warehouses or Data Lakes.
The best one we’ve come across is this one here: Learn SQL for Data Analysis with Google Big Query.
Who is it for?
If you are already familiar with basic Machine Learning and what you are for is consolidating and improving your knowledge, learning Tensorflow in Depth, or how to program different algorithms, this is probably not the course for you, and we recommend other resources like the book “Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow”. It is a great book for beginers and even for Medium practitioners to learn all about Machine Learning using its most popular libraries.
What this course will provide is a brief introduction on how to leverage the power of Google Cloud Platform to impulse your existing Machine Learning knowledge and build real world applications, train models using advanced hardware, or get your ML software production ready.
Basically, it targets data scientists, people starting their journey in the ML/DL domain, and anyone keen to start training deep neural networks using cloud infrastructures. After completing this course, you can maybe try some Udemy Bigquery courses, to learn about ETLs and other tools that Google offers on their Google Cloud Platform.
There are no real pre-requisites needed to complete this course, however, familiarity with ML concepts will be advantageous. No programming experience is needed either, however it is best to have some previous Python knowledge.
If you don’t think you have this knowledge, check out our awesome section on Python books.
Google Cloud Machine Learning with tensorflow is a course that in our opinion is worth doing if you can get it for free, as you can complete it in under a week with little to no effort, or even paying the 10€ that is aproximatly worth most of the time on Udemy. However, we don’t recommend doing it without this discount, as the value that it can provide is very limited.
If you are looking for an online course to learn Machine Learning this is definitely not it, and we would recommend taking a look at our Machine Learning online-courses section and searching for something different.
With this (excessively brief) video course, you will use the power of Google’s Cloud Platform to train deep neural networks faster, how to set models up for production and how to connect the different services of GCP, however, you will not become an expert at it until you get working on a real project using it, which unfortunatly the course does not offer.
The tone of the course and the videos are quality material, however the contents explained are very brief in our opinon, and will leave you wanting more.
That is all, thank you for reading How to Learn Machine Learning, and we hope you found the review useful.
About the Author
Tobias Zwingmann is Senior Data Scientist at Deutsche Messe AG and possesses a high entrepreneurial bias. In his work, he utilizes small and big data to create value—for example, by turning analytical insights into better customer experience and higher customer engagement. He also supports the development of new data-driven businesses and prototypes new data products for his organization. He likes to learn from and meet open-minded people.
Tags: Udemy BigQuery Course, Udemy Tensorflow Course, Bigquery Udemy, Google Cloud Platform for Machine Learning, Tensorflow Course, Tensorflow Udemy.