Create ML Models with BigQuery ML
Master Machine Learning with BigQuery ML: From Data to Predictions
- Practical ML Application
- Create models without extensive coding
- Ideal for SQL-savvy analysts
- Without infrastructure management
- Enhance your skills in data engineering, analytics, or AI-driven decision-making
- Align with industry
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Google Cloud certifications in AI
What you will learn
The "Create ML Models with BigQuery ML" skill badge course on Google Cloud Skills Boost equips learners with hands-on experience in building, evaluating, and deploying machine learning models directly within Google BigQuery. Designed for data professionals, this intermediate-level program focuses on leveraging BigQuery ML—a tool that simplifies ML workflows using SQL—to solve real-world prediction tasks like customer purchase behavior, taxi fare forecasting, and sports bracketology. By completing five structured labs, participants gain practical expertise in classification, regression, and time-series modeling while working with large-scale public datasets.
Google's Beginner-friendly Create ML Models with BigQuery ML course Outline
Learn the Create ML Models with BigQuery ML
Google Cloud's "Create ML Models with BigQuery ML" offers a streamlined path for those looking to upskill in machine learning without leaving their data warehouse. This course bridges SQL expertise with predictive analytics, enabling you to build models like customer purchase predictors or fare estimators directly in BigQuery. With hands-on labs and a focus on real datasets, it's perfect for data analysts and engineers aiming to add ML to their toolkit. Ready to transform raw data into actionable insights? Explore the course here and earn a credential that validates your skills in scalable, SQL-driven machine learning.
Start your journey today:
Course Structure Includes:
The curriculum includes five interactive labs:
- Getting Started with BigQuery ML: Build a binary classification model to predict visitor transactions.
- Predict Visitor Purchases: Use logistic regression on ecommerce data to forecast customer behavior.
- Predict Taxi Fare: Develop a time-series model with NYC taxi data to estimate trip costs.
- Bracketology with ML: Analyze NCAA sports data to predict tournament outcomes.
- Challenge Lab: Test your skills in an open-ended ML project.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn to build, evaluate, and deploy ML models using SQL in Google BigQuery. Tackle real-world scenarios like customer behavior prediction and fare forecasting with hands-on labs.
Pre-requisites
No Experience needed.
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SPOTO Empowers You to Earn Your Certification.
Benefits of Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer certification validates expertise in designing, building, and deploying machine learning models using Google Cloud technologies. It demonstrates proficiency in critical areas such as framing ML problems, architecting scalable solutions, data preparation, model development, and productionization. This certification is highly regarded in the industry, enhancing career prospects by signaling advanced technical skills and practical experience in solving real-world business challenges. Certified professionals often gain a competitive edge in roles like machine learning engineer, data scientist, or AI developer, with opportunities at leading global companies. Additionally, Google recommends at least three years of ML experience for the exam, ensuring that certified individuals possess both theoretical knowledge and hands-on capabilities.
Advantages of Using SPOTO's Exam Preparation Materials
SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. Their materials likely include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on Google Cloud tools and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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