English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 27m | 178 MB
BigQuery ML on the Google Cloud Platform democratizes machine learning by allowing data analysts and engineers to build and use machine learning models directly from SQL without using any higher level programming language.
This course demonstrates how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, the Google Cloud Platform’s serverless data warehouse. In this course, Building Machine Learning Models in SQL Using BigQuery ML, you’ll learn how to build and train machine learning models and how to employ those models for prediction – all with just simple SQL commands on data stored in BigQuery. First, you’ll understand the different choices available on the GCP if you would like to build and train your models and see how you can make the right choice between these services for your specific use case. Then, you’ll work with some real-world datasets stored in BigQuery to build linear regression and binary classification models. Because BigQuery allows you to specify training parameters to build and train your model in SQL, machine learning is made accessible to even those who are not familiar with high-level programming languages. Last, you’ll study how to analyze the models that we built using evaluation and feature inspection functions in BigQuery, and run BigQuery commands on Cloud Datalab using a Jupyter notebook that is hosted on the GCP and closely integrated with all of GCPs services. By the end of this course, you’ll have a good understanding of how you can use BigQuery ML to extract insights from your data by applying linear and logistic regression models.
Table of Contents
Course Overview
1 Course Overview
Introducing Google BigQuery ML
2 Module Overview
3 Prerequisites and Course Outline
4 Democratizing Machine Learning with BigQuery ML
5 BigQuery ML vs. Other Google AI Services
6 Logging into the GCP
7 Uploading Reviews to Cloud Shell
8 Creating Datasets and Tables Loading and Querying Data
9 Running Queries and Visualizing Results Using Data Studio
Building Regression and Classification Models
10 Module Overview
11 Linear Regression
12 Logistic Regression
13 Building Linear and Logistic Regression Models in BigQuery ML
14 Creating and Loading a Table with Data
15 Creating and Training a Regression Model
16 Evaluating the Regression Model
17 Predictions and Data Visualization
18 Accuracy Precision and Recall Using the Confusion Matrix
19 Creating and Training a Classification Model
20 Evaluating the Classifier and Using It for Prediction
Analyzing Models Using Evaluation and Feature Inspection Functions
21 Module Overview
22 Creating and Connecting to a Datalab Instance
23 Using Cloud Datalab to Build BigQuery ML Models
24 The ROC Curve
25 Exploring Adult Salary Data for Classification
26 Evaluating Classifiers Using the ROC Curve
27 Summary and Further Study
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