At Google I/O, the global tech giant announced a bunch of free courses to help budding developers explore the potential of machine learning and artificial intelligence technology across various open-source frameworks and platforms like TensorFlow.js, TensorFlow Lite, Vertex.AI etc.
We have made a list of all the machine learning and artificial intelligence courses announced at Google I/O.
[For beginner]
It is an excellent course for beginners, especially if you want to solve the spam issue. It will introduce you to TensorFlow.js and machine learning and help you build a comment-spam detection system using TensorFlow.js.
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Here, you will learn the concepts behind machine learning and identify spam using text classification ML. Besides this, you will also learn how to:
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In this tutorial, you will get to build your first computer-vision app on Android or iOS and find out how an image classifier capable of recognising hundreds of different types of images is created. Build your first computer-vision app on Android or iOS.
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In this course, you will learn about object detection and how it differs from other image-recognition tasks, such as image classification. It will provide you with learning essentials that will help you build an object detector into your mobile app and integrate an object detector using ML kit object detection API.
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Here, you will learn how to use machine learning for Audio Classification, add audio classification to your mobile app and create a basic app for audio classification.
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[For intermediate]
Here, you will learn how to customise an audio classification model, particularly a pre-trained audio classification model to detect bird sounds.
In this tutorial, you will get to train an object-detection model using your dataset and deploy it to a mobile application using TensorFlow Lite.
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Learn how to build a custom model for image classification using TensorFlow Lite Model Maker and integrate it into an app as a custom ML Kit Model.
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In this tutorial, you will learn how to retrain your comment spam classification model to account for instances it does not classify correctly using TensorFlow.js.
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You will learn how to build and call a product search backend from the mobile app using Vision API Product Search.
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[Advanced]
Using this coding exercise, you will get to build trusted AI products with the PAIR (People + AI Research) guidebook.
Besides learning to build trustworthy, user-centred AI products using PAIR Guidebook, you will also learn:
Written by Khanh LeViet, a TensorFlow evangelist at Google, this course will help you train a custom object detection model using a set of training images with TFLite Model Maker, and deploy the model using TFLite Task Library.
Here are the highlights of the course:
In this tutorial, you’ll learn to build a TensorFlow.js model to recognise handwritten digits with a convolutional neural network. You will also learn to use supervised learning. The course is designed by Google software engineer Yannick Assogba.
Here are the things you will learn from this course:
Written by Assogba, this coding exercise will help you train a model to make predictions from numerical data describing a set of cars. With this course, you will get familiar with the basic terminology, concepts and syntax around training models with TensorFlow.js.
At the end of this course, you will learn:
This coding exercise will help you create a webpage that uses machine learning directly in the web browser via TensorFlow.js to classify and detect common objects from a live webcam stream, where it will be able to identify, objectify and get the coordinates of the bounding box for each object it finds.
In this course, you will earn how to:
In this coding exercise, you will get familiar with Firebase Hosting. Moreover, you will be able to develop an end-to-end system capable of hosting and running a custom saved TensorFlow.js model along with its related assets such as HTML, CSS and JavaScript.
Additionally, you will be able to create a simple, lightweight model and host it via Firebase Hosting. You will learn how to:
Written by a software engineer at Google Nikhil Thorat, in this coding exercise, you will learn how to build a simple teachable machine. In this course, you will learn:
Here, you will learn how to use Vertex.AI, a newly announced managed ML platform by Google Cloud, to build end-to-end machine learning workflows.
At the end of this exercise, you will learn how to:
Note: The total cost to run this exercise on Google Cloud is about $2.
Amit Raja Naik is a senior writer at Analytics India Magazine, where he dives deep into the latest technology innovations. He is also a professional bass player.
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