Designing and Implementing Solutions Using Google Cloud AutoML

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.
Course info
Level
Beginner
Updated
Oct 12, 2018
Duration
1h 41m
Table of contents
Description
Course info
Level
Beginner
Updated
Oct 12, 2018
Duration
1h 41m
Description

Most organizations want to harness the power of machine learning in order to improve their products, but they may not always have the expertise available in-house. In this course, Designing and Implementing Solutions Using Google Cloud AutoML, you’ll learn how you can train custom machine learning models on your dataset with just a few clicks on the UI or a few commands on a terminal window. This course will also show how engineers and analysts can harness the power of ML for common use cases by using AutoML to build their own model, trained on their own data, without needing any specific machine learning expertise. First, you'll see an overview of the suite of machine learning services available on the Google Cloud and understand the features of each so you can make the right choice of service for your use case. You’ll learn about the basic concepts underlying AutoML which uses neural architecture search and transfer learning to find the best neural network for your custom use case. Next, you'll explore AutoML’s translation model, and feed in sentence pairs to the TMX format to perform German-English translation. You’ll use your custom model for prediction from the UI, from the command line, and by using Python APIs. You’ll also learn to understand the significance of the BLEU score to analyze the quality of your translation model. Finally, you'll use the natural language APIs that AutoML offers to build a model for sentiment analysis of reviews and work with AutoML for image classification using the AutoML Vision APIs. You'll finish up by learning the basic requirements of the data needed to train this model and develop a classifier that can identify fruits. At the end of this course, you will be very comfortable choosing the right ML API that fits your use case and using AutoML to build complex neural networks trained on your own dataset for common problems.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Designing and Implementing Solutions Using Google Cloud AutoML. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. In this course you'll learn how you can train custom machine learning models on your dataset with just a few clicks on the UI or a few commands on the terminal window. You'll build powerful models without having any specialized ML expertise. We'll start this course off with an overview of the suite of machine learning services available on the Google Cloud and understand the features of it so we can make the right choice of service for our use case. We'll understand the basic concepts underlying AutoML, which uses neural architecture search and transfer learning to find the best neural network for our custom use case. We'll use AutoML's translation model and feed in sentence pairs in the TMX format to perform German/English translation. We'll use our custom model for prediction from the user interface, from the command line, and using Python APIs. We'll also understand the significance of the BLEU score to analyze the quality of our translation model. We'll then use the natural language APIs that AutoML offers to build a model for sentiment analysis of reviews. We'll study the use of the confusion matrix and precision and recall, along with the model threshold to analyze the quality of our classification model. We'll then work with AutoML for image classification using the AutoML Vision APIs, we'll study the basic requirements of the data needed to train this model, and develop a classifier that can identify fruits. At the end of this course, you will be very comfortable choosing the right ML API that fits your use case and using AutoML to build complex neural networks trained on your own dataset.