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Janani Ravi
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/nttte4/building
What's inside
Syllabus
Course Overview
Introducing Google BigQuery ML
Building Regression and Classification Models
Analyzing Models Using Evaluation and Feature Inspection Functions
Read more
Syllabus
Course Overview
Introducing Google BigQuery ML
Building Regression and Classification Models
Analyzing Models Using Evaluation and Feature Inspection Functions
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills sought after by industry for Data Analyst and Data Engineer roles
Teaches foundational concepts in machine learning and their applications in data analysis
Uses SQL commands, making it accessible to data analysts and engineers
Emphasizes hands-on learning through real-world datasets
Taught by industry experts Janani Ravi
Introduces BigQuery ML, a service for building machine learning models on Google Cloud Platform
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Building Machine Learning Models in SQL Using BigQuery ML with these
activities:
Follow Tutorials on BigQuery ML
Show steps
Enhance your understanding of BigQuery ML by following guided tutorials that provide step-by-step instructions.
Browse courses on
BigQuery ML
Show steps
-
Search for tutorials on the official BigQuery ML website or other reputable sources.
-
Follow the instructions in the tutorials to build and train machine learning models.
Review SQL concepts
Show steps
Reviewing SQL concepts will prepare you for building machine learning models using SQL commands in BigQuery.
Browse courses on
SQL
Show steps
-
Review the basics of SQL syntax, data types, and operators.
-
Practice writing and executing SQL queries to retrieve data from tables.
-
Experiment with different SQL functions for data manipulation and aggregation.
Review machine learning concepts
Show steps
Refreshing your knowledge of machine learning concepts will enhance your understanding of how to build and use machine learning models effectively.
Browse courses on
Machine Learning
Show steps
-
Review the basics of machine learning algorithms, including linear regression and binary classification.
-
Review the key performance metrics used to evaluate machine learning models.
-
Experiment with different machine learning algorithms using online platforms or tools.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Solve SQL Queries for Machine Learning
Show steps
Practice your SQL skills by solving queries related to machine learning and data analysis in BigQuery.
Browse courses on
SQL
Show steps
-
Find practice problems or exercises online.
-
Solve the problems using SQL commands on BigQuery.
Learn about BigQuery ML
Show steps
Following guided tutorials will supplement your understanding of how to build and train machine learning models using BigQuery ML.
Browse courses on
BigQuery ML
Show steps
-
Find and follow online tutorials on BigQuery ML.
-
Complete the tutorials to practice building and training machine learning models using SQL.
Participate in a study group
Show steps
Engaging with peers through study groups can provide diverse perspectives, enhance understanding, and improve retention.
Browse courses on
Collaboration
Show steps
-
Find or form a study group with other learners taking the same course.
-
Meet regularly to discuss course materials, share insights, and work on assignments together.
-
Take turns leading discussions and presenting on different topics.
Build an Interactive Data Visualization
Show steps
Create a data visualization using BigQuery ML to practice building and interpreting machine learning models.
Browse courses on
Machine Learning
Show steps
-
Gather and prepare your data in BigQuery.
-
Build a machine learning model using BigQuery ML.
-
Create a data visualization that displays the model's predictions.
Build and train linear regression models
Show steps
Working through practice drills will reinforce your ability to build and train linear regression models for predicting continuous values.
Browse courses on
Linear Regression
Show steps
-
Prepare a dataset with numerical features and a target variable.
-
Write SQL queries to build and train linear regression models using BigQuery ML.
-
Evaluate the performance of the trained models.
-
Practice tuning the hyperparameters of the models for better accuracy.
Build and train binary classification models
Show steps
Working through practice drills will reinforce your ability to build and train binary classification models for predicting binary outcomes.
Browse courses on
Binary Classification
Show steps
-
Prepare a dataset with categorical features and a binary target variable.
-
Write SQL queries to build and train binary classification models using BigQuery ML.
-
Evaluate the performance of the trained models using metrics like accuracy and F1-score.
-
Practice tuning the hyperparameters of the models for better performance.
Attend industry meetups
Show steps
Attending industry meetups will expose you to professionals in the field, expand your knowledge, and provide opportunities to learn from others.
Browse courses on
Networking
Show steps
-
Research and identify industry meetups related to machine learning or data analysis.
-
Attend meetups regularly to connect with professionals, learn about new trends, and share your knowledge.
-
Follow up with individuals you meet at meetups to build relationships and stay informed.
Build a machine learning model for a real-world dataset
Show steps
Applying your skills to a real-world dataset will provide practical experience and demonstrate your understanding of building and using machine learning models in BigQuery.
Show steps
-
Choose a real-world dataset that aligns with your interests or industry.
-
Explore the dataset and identify a problem or question that you can solve using machine learning.
-
Build and train a machine learning model using BigQuery ML to address the problem or answer the question.
-
Evaluate the performance of your model and interpret the results.
-
Write a report or presentation to document your project and findings.
Follow Tutorials on BigQuery ML
Show steps
Enhance your understanding of BigQuery ML by following guided tutorials that provide step-by-step instructions.
Browse courses on
BigQuery ML
Show steps
Show steps
Show steps
- Search for tutorials on the official BigQuery ML website or other reputable sources.
- Follow the instructions in the tutorials to build and train machine learning models.
Review SQL concepts
Show steps
Reviewing SQL concepts will prepare you for building machine learning models using SQL commands in BigQuery.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Review the basics of SQL syntax, data types, and operators.
- Practice writing and executing SQL queries to retrieve data from tables.
- Experiment with different SQL functions for data manipulation and aggregation.
Review machine learning concepts
Show steps
Refreshing your knowledge of machine learning concepts will enhance your understanding of how to build and use machine learning models effectively.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the basics of machine learning algorithms, including linear regression and binary classification.
- Review the key performance metrics used to evaluate machine learning models.
- Experiment with different machine learning algorithms using online platforms or tools.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Solve SQL Queries for Machine Learning
Show steps
Practice your SQL skills by solving queries related to machine learning and data analysis in BigQuery.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Find practice problems or exercises online.
- Solve the problems using SQL commands on BigQuery.
Learn about BigQuery ML
Show steps
Following guided tutorials will supplement your understanding of how to build and train machine learning models using BigQuery ML.
Browse courses on
BigQuery ML
Show steps
Show steps
Show steps
- Find and follow online tutorials on BigQuery ML.
- Complete the tutorials to practice building and training machine learning models using SQL.
Participate in a study group
Show steps
Engaging with peers through study groups can provide diverse perspectives, enhance understanding, and improve retention.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find or form a study group with other learners taking the same course.
- Meet regularly to discuss course materials, share insights, and work on assignments together.
- Take turns leading discussions and presenting on different topics.
Build an Interactive Data Visualization
Show steps
Create a data visualization using BigQuery ML to practice building and interpreting machine learning models.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and prepare your data in BigQuery.
- Build a machine learning model using BigQuery ML.
- Create a data visualization that displays the model's predictions.
Build and train linear regression models
Show steps
Working through practice drills will reinforce your ability to build and train linear regression models for predicting continuous values.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Prepare a dataset with numerical features and a target variable.
- Write SQL queries to build and train linear regression models using BigQuery ML.
- Evaluate the performance of the trained models.
- Practice tuning the hyperparameters of the models for better accuracy.
Build and train binary classification models
Show steps
Working through practice drills will reinforce your ability to build and train binary classification models for predicting binary outcomes.
Browse courses on
Binary Classification
Show steps
Show steps
Show steps
- Prepare a dataset with categorical features and a binary target variable.
- Write SQL queries to build and train binary classification models using BigQuery ML.
- Evaluate the performance of the trained models using metrics like accuracy and F1-score.
- Practice tuning the hyperparameters of the models for better performance.
Attend industry meetups
Show steps
Attending industry meetups will expose you to professionals in the field, expand your knowledge, and provide opportunities to learn from others.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research and identify industry meetups related to machine learning or data analysis.
- Attend meetups regularly to connect with professionals, learn about new trends, and share your knowledge.
- Follow up with individuals you meet at meetups to build relationships and stay informed.
Build a machine learning model for a real-world dataset
Show steps
Applying your skills to a real-world dataset will provide practical experience and demonstrate your understanding of building and using machine learning models in BigQuery.
Show steps
Show steps
Show steps
- Choose a real-world dataset that aligns with your interests or industry.
- Explore the dataset and identify a problem or question that you can solve using machine learning.
- Build and train a machine learning model using BigQuery ML to address the problem or answer the question.
- Evaluate the performance of your model and interpret the results.
- Write a report or presentation to document your project and findings.
Career center
Learners who complete Building Machine Learning Models in SQL Using BigQuery ML will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Data Scientist
Data Scientist
A Data Scientist uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. Even though Data Scientists use advanced programming languages, this course can teach them how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This may be helpful for someone who wishes to break into this field.
Data Engineer
Data Engineer
A Data Engineer uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This may be helpful for someone who wishes to break into this field.
Data Analyst
Data Analyst
A Data Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This can help a Data Analyst add new skills to their resume.
Market Research Analyst
Market Research Analyst
A Market Research Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Business Analyst
Business Analyst
A Business Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Actuary
Actuary
An Actuary uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Statistician
Statistician
A Statistician uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be useful for someone who wishes to break into this field.
Financial Analyst
Financial Analyst
A Financial Analyst uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Product Manager
Product Manager
A Product Manager uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Software Engineer
Software Engineer
A Software Engineer uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Data Architect
Data Architect
A Data Architect uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
Database Administrator
Database Administrator
A Database Administrator uses SQL to extract data from BigQuery and manipulate it in various ways. This course, Building Machine Learning Models in SQL Using BigQuery ML, is a foundational block for someone wishing to enter this career field. In this course, one can learn how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, Google Cloud Platform’s serverless data warehouse. This course may be helpful for someone who wishes to break into this field.
For more career information including salaries, visit:
OpenCourser.com/course/nttte4/building
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Building Machine Learning Models in SQL Using BigQuery ML.
Covers advanced analytics techniques using Apache Spark, including machine learning and data mining. While it does not focus specifically on BigQuery ML, it provides a solid foundation for understanding machine learning concepts and algorithms, which can be applied to BigQuery ML.
Provides a comprehensive overview of machine learning algorithms, including linear regression and logistic regression. It useful resource for understanding the theoretical foundations of machine learning and how these algorithms work.
Covers machine learning concepts and techniques using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. While it does not focus specifically on BigQuery ML, it provides valuable insights into machine learning implementation and best practices.
Covers machine learning concepts and techniques using Python. While it does not focus specifically on BigQuery ML, it provides a practical guide to building and deploying machine learning models using Python, which can be applied to BigQuery ML.
Covers deep learning concepts and techniques using Python and the Keras library. While it does not focus specifically on BigQuery ML, it provides valuable insights into deep learning models and their applications.
Covers statistical methods and techniques used in machine learning. While it does not focus specifically on BigQuery ML, it provides a solid foundation in statistical methods and their applications in machine learning.
Covers machine learning from a probabilistic perspective, focusing on Bayesian methods. While it does not focus specifically on BigQuery ML, it provides valuable insights into probabilistic models and their applications in machine learning.
Covers a wide range of statistical and machine learning methods, including linear regression, logistic regression, and decision trees. While it does not focus specifically on BigQuery ML, it provides a comprehensive overview of statistical learning methods and their applications.
Save
Covers big data analytics from a business and technology perspective. While it does not focus specifically on BigQuery ML, it provides valuable insights into the challenges and opportunities of big data analytics and its applications in various industries.
Covers machine learning concepts and techniques using the R programming language. While it does not focus specifically on BigQuery ML, it provides a practical guide to building and deploying machine learning models using R, which can be applied to BigQuery ML.
Covers data analysis using Python. While it does not focus specifically on BigQuery ML, it provides a practical guide to data analysis techniques using Python, which can be applied to BigQuery ML.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nttte4/building
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Time
87 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Google: Professional Cloud Data Engineer. It's comprised of 18 courses:
- Architecting Big Data Solutions Using Google Dataproc
- Architecting Serverless Big Data Solutions Using Google Dataflow
- Architecting Data Warehousing Solutions Using Google BigQuery
- Building Machine Learning Models in SQL Using BigQuery ML (viewing)
- Creating and Administering Google Cloud SQL Instances
- Architecting Big Data Solutions Using Google Bigtable
- Designing and Implementing Solutions Using Google Machine Learning APIs
- Designing and Implementing Solutions Using Google Cloud AutoML
- Building Pipelines for Workflow Orchestration Using Google Composer
- Architecting Stream Processing Solutions Using Google Cloud Pub/Sub
- Creating and Administering Google Cloud Spanner Instances
- Architecting Schemaless Scalable NoSQL Databases Using Google Datastore
- Leveraging Fully Managed Redis Datastores Using Google Cloud Memorystore
- Leveraging Google Cloud Firestore for Realtime Database Solutions
- Architecting Scalable Web Applications with Firebase on the Google Cloud Platform
- Developing on the Google Cloud Using Datalab and Cloud Source Repositories
- Architecting Production-ready ML Models Using Google Cloud ML Engine
- Designing Scalable Data Architectures on the Google Cloud
Good to know
Develops skills sought after by industry for Data Analyst and Data Engineer roles
Teaches foundational concepts in machine learning and their applications in data analysis
Uses SQL commands, making it accessible to data analysts and engineers
Emphasizes hands-on learning through real-world datasets
Taught by industry experts Janani Ravi
Introduces BigQuery ML, a service for building machine learning models on Google Cloud Platform
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