Machine Learning on Google Cloud Platform
Learning Path Description
This learning path will introduce you to Machine Learning. You will find out how to create and train your own models using a Jupyter notebook. Also, you will learn how create models using the Google Vertex AI platform. Even if you don't have any previous experience with machine learning, that's okay.
The first course explains the fundamentals. You will discover the differences between Artificial Intelligence, Machine Learning and Deep Learning. And you will learn how Machine Learning actually works, as well as the types of problems it can be used to solve.
The second course is a live demonstration that walks you through how to create your own machine learning model. You will learn how to set up your environment, import your data, create appropriate features, train your model, and make multiple refinements.
The third course shows you how you can simplify your Machine Learning tasks by using Vertex AI. The main exciting feature here is AutoML. This allows you to create high-quality models without writing any code. All you have to do is feed in your dataset.
- Understand the basics of machine learning
- Train and evaluate your own machine learning model
- Improve the performance of your model
- Create a managed dataset in Vertex AI
- Use AutoML to train a model
- Deploy a trained model using Vertex AI to make predictions
- Data Engineers
- Machine Learning Engineers
- Google Cloud Platform account recommended (You can sign up for free trial)
If you have thoughts or suggestions for this learning path, please contact Cloud Academy at firstname.lastname@example.org.
Daniel began his career as a Software Engineer, focusing mostly on web and mobile development. After twenty years of dealing with insufficient training and fragmented documentation, he decided to use his extensive experience to help the next generation of engineers.
Daniel has spent his most recent years designing and running technical classes for both Amazon and Microsoft. Today at Cloud Academy, he is working on building out an extensive Google Cloud training library.
When he isn’t working or tinkering in his home lab, Daniel enjoys BBQing, target shooting, and watching classic movies.