Feature Engineering: From Raw Data to ML-Ready Features
Transform Data, Transform Models: Engineer Features Like a Pro.
- Expertise in Feature Engineering
- Master techniques to transform raw data into high-quality features
- Gain experience with Google Cloud tools
- Scalable Solutions
- Reducing redundancy in large-scale projects
- 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 "Feature Engineering" course on Google Cloud Skills Boost equips learners with advanced techniques to optimize machine learning models through effective data transformation. Designed for data professionals, this course focuses on leveraging Vertex AI Feature Store for centralized feature management and teaches practical skills to identify, create, and refine high-impact features using BigQuery ML, Keras, TensorFlow, and Dataflow. Participants will learn to bridge raw data to actionable insights, covering feature crosses, preprocessing with Apache Beam, and automated workflows with TensorFlow Transform (tf.Transform). Ideal for those aiming to enhance model accuracy and streamline ML pipelines.
Google's Beginner-friendly Feature Engineering course Outline
Learn the Feature Engineering
For data professionals seeking to unlock the full potential of machine learning models, Google Cloud's "Feature Engineering" course is a must. Dive into tools like Vertex AI Feature Store to centralize and manage features efficiently, while mastering techniques to transform raw data into predictive gold using BigQuery ML and TensorFlow. Whether you're optimizing preprocessing pipelines or tackling feature crosses, this course blends theory with hands-on labs to elevate your ML workflows. Ready to turn data into your greatest asset? Explore the course here and earn a credential that showcases your mastery of feature engineering in the cloud.
Start your journey today:
Course Structure Includes:
The curriculum includes seven modules with practical labs and case studies:
- Introduction to Vertex AI Feature Store: Centralize, share, and reuse ML features across teams.
- Raw Data to Features: Learn feature representation, embedding techniques, and evaluation metrics.
- Feature Engineering with BigQuery ML & Keras: Build and optimize features using SQL and neural networks.
- Preprocessing with Dataflow & Dataprep: Automate ETL workflows for large datasets.
- Feature Crosses: Solve complex problems by combining features in TensorFlow Playground.
- TensorFlow Transform (tf.Transform): Preprocess data at scale for TensorFlow models.
- Challenge Labs: Apply skills to real-world scenarios like customer behavior prediction.
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, manage, and optimize ML features using Vertex AI, BigQuery ML, and TensorFlow. Elevate model accuracy with hands-on labs and industry best practices.
Pre-requisites
No Experience needed.
Generative AI in Real World Workplaces PDF Free Download | SPOTO
Generative AI in Real World Workplaces PDF Free Download | SPOTO
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Governing AI- A Blueprint for the Future PDF Free Download | SPOTO
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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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