In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
Overview
Syllabus
- Introduction
- Course Introduction
- Introduction to Machine Learning
- Introduction to Machine Learning
- Demo: Google Photos
- What is deep learning?
- ML applications for business
- ML terms: Instances, features, labels
- The spectrum of ML tools
- Introduction to Machine Learning
- Pre-trained ML APIs
- Pre-trained ML models
- Demo: Cloud Translate and Vision APIs
- Explore the Vision API
- Demo: Natural Language Processing API
- Explore the Language APIs
- Training with Pre-built ML Models using Cloud Vision API and AutoML
- Pre-trained ML models
- Creating ​ML Datasets in BigQuery
- What makes a dataset good for ML?
- Choosing good features
- Exploring and preprocessing data
- Other tools for creating data pipelines
- Knowing the unknowable
- Creating repeatable dataset splits
- Creating Repeatable Dataset Splits in BigQuery v1.5
- Creating ​ML Datasets in BigQuery
- Creating ML Models in BigQuery
- Introducing BigQuery machine learning
- Phases of model building
- BigQuery ML resources
- Lab Intro: Predicting Visitor Return Purchases with BigQuery
- Predict Visitor Purchases with a Classification Model in BQML v1.5
- Predict Taxi Fare with a BigQuery ML Forecasting Model v1.5
- BigQuery ML
- End of Course Recap
- Course Summary
- Course Resources
- Course Resources
- Your Next Steps
- Course Badge
Tags
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