Free Courses That Are Actually Free: Google Cloud Edition

testv September 23, 2024

0 2 6 minutes read

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If you’ve been keeping up, I have been creating a series of free courses that are actually free, for example, the AI & ML Edition. Type in ‘Free courses that are actually free’ in the search bar to look at the rest. In this blog, I will dive into free courses with Google, from programming languages to cloud computing.

Google AI for Anyone

Link: Google AI for Anyone

As its name suggests, this course is for anybody. You don’t need a computer science, mathematics or AI background to understand it. You also do not need any programming skills.

In this course, Google will take you through the principles of AI and what the fuss is all about. You’ll get hands-on in playing with data to teach a computer how to recognise images, sounds and more. As you explore how AI is used in the real world (recommender systems, computer vision, self-driving etc.) you will also begin to build an understanding of Neural networks and the types of machine learning including supervised, unsupervised, reinforcement etc. You will also see (and experience) what programming AI looks like and how it is applied.

Cloud Computing Fundamentals

Link: Cloud Computing Fundamentals

We already know how cloud Computing is revolutionising today’s world. It affects the way we communicate, do business, and interact with everyday things and one another.

In this course offered by Google, it will go through the fundamental theoretical and practical applications of Cloud Computing. It provides the basic concepts to understand why cloud computing is and will be such an important part of future jobs and businesses, as well as a focus on Google Cloud’s extended solutions and compute options.

Infrastructure in Google Cloud

Link: Infrastructure in Google Cloud

This second course in the Google Cloud Computing Foundations Professional Certificate, this course reviews implementing storage models, different application-managed service options, and security administration in Google Cloud.

This course focuses on the Infrastructure components that allow Cloud Computing to exist. It will cover concepts such as differences between Physical/Virtual Hardware, Servers, VMware, Database and Storage solutions. It will provide a basic understanding of the solutions and options available in Google Cloud Platform.

Networking and Security in Google Cloud

Link: Networking and Security in Google Cloud

This third course in the Google Cloud Computing Foundations Professional Certificate covers how to build secure networks, and cloud automation and management tools.

This course focuses on the fundamental elements that every network requires: privacy, security, compliance and availability. This course demonstrates how to set up security measures for a more secure and robust network using Google Cloud.

Data, ML, and AI in Google Cloud

Link: Data, ML, and AI in Google Cloud

Data is the most important asset in the Cloud Computing era. This course focuses on the collection, storage and management of big data. It provides a comprehensible frame structure for Data Analysis. It also explains how to configure and make extensive use of Machine Learning and Artificial Intelligence by using Google Cloud.

This final course in the Google Cloud Computing Foundations Professional Certificate reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud.

AI for JavaScript developers with TensorFlow.js

Link: AI for JavaScript developers with TensorFlow.js

Are you a web engineer, designer, or creative thinker looking to apply AI or use Machine Learning? This course offers a solution and the knowledge to be the “missing manual” for JavaScript users without a background in Machine Learning.

This course aims to educate, inspire, and enable you to rapidly create your next ML-powered idea in this rapidly emerging industry while providing you with a solid foundation to understand the field and the confidence to explore the industry further.

Google Cloud Big Data and Machine Learning Fundamentals

Link: Google Cloud Big Data and Machine Learning Fundamentals

Are you a data analyst, data engineer, data scientist, or ML engineer who is getting started with Google Cloud?

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Smart Analytics, Machine Learning, and AI on Google Cloud

Link: Smart Analytics, Machine Learning, and AI on Google Cloud

This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customisation required.

For little to no customisation, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.

Getting Started with Terraform for Google Cloud

Link: Getting Started with Terraform for Google Cloud

This course is aimed at DevOps engineers, Cloud architects, and Cloud engineers who want to start using Terraform to automate infrastructure provisioning with a focus on Google Cloud Platform.

This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure. Learners will get hands-on practice building and managing Google Cloud resources using Terraform.

Modernizing Data Lakes and Data Warehouses with Google Cloud

Link: Modernizing Data Lakes and Data Warehouses with Google Cloud

This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports, for example, data engineers, data analysts, database administrators, and big data architects.

This course highlights use cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. You will also dive into the role of a data engineer, the benefits of a successful data pipeline to business operations, and examine why data engineering should be done in a cloud environment.

Getting Started with Google Kubernetes Engine

Link: Getting Started with Google Kubernetes Engine

This course is intended for the following job roles: application developers, cloud solutions architects, DevOps engineers, and IT managers. Also for individuals using Google Cloud to create new solutions or to integrate existing systems, application environments, and infrastructure with Google Cloud.

In the first module, you’ll be introduced to a range of Google Cloud services and features, to help you choose the right Google Cloud services to create your cloud solution. You’ll learn about creating a container using Cloud Build and storing a container in the Container Registry. You’ll also compare and contrast the features of Kubernetes and Google Kubernetes Engine, also known as GKE. In addition to conceptualising the Kubernetes architecture, you’ll deploy a Kubernetes cluster using GKE, deploy Pods to a GKE cluster, and view and manage Kubernetes objects.

Building Batch Data Pipelines on Google Cloud

Link: Building Batch Data Pipelines on Google Cloud

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data.

Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

Wrapping up

A wide range of free courses are offered by Google to learn about their cloud platform as well as the fundamentals of AI. Some of these courses are part of a personalised professional certificate, which would be a bonus to add to your resume!

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.