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.
What you'll learn
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.
Table of contents
- Module Overview 1m
- Cloud ML APIs vs. AutoML for Translation 7m
- Enabling APIs for AutoML Translation 4m
- Creating a Dataset and Importing Data 3m
- Training and Prediction 3m
- Getting Service Account Credentials 2m
- Prediction Using the REST API and Python Client 5m
- Cleaning up Models, Datasets, and Buckets 1m
- Using the Command Line to Train a Model 4m
- Creating Dataset and Importing Data Using the Command Line 5m
- Prediction Using the Command Line 4m
- Module Overview 1m
- Cloud ML APIs vs. AutoML for Text 2m
- Confusion Matrix: Accuracy, Precision, and Recall 6m
- Probability Threshold for Classification 2m
- Types of Classifiers 1m
- Create Dataset and Upload Training Data 3m
- Training the Sentiment Analysis Model 3m
- Evaluating the Classifer and Using It for Prediction 5m