Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations
β±οΈ Length: 16.5 total hours
β 4.33/5 rating
π₯ 41,920 students
π July 2023 update
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- Course Overview
- This program rigorously prepares participants for the Google Certified Professional Machine Learning Engineer exam, covering the end-to-end lifecycle of ML solutions on Google Cloud Platform. It integrates architectural design, implementation, deployment, and operationalizing ML systems.
- Master Google Cloud AI/ML services like Vertex AI and TensorFlow, alongside best practices for data processing, model training, evaluation, and MLOps. The curriculum focuses on building scalable, high-performance, and responsible AI/ML solutions in real-world scenarios.
- With 16.5 hours of content and 137 practice questions, this course empowers aspiring ML engineers to achieve professional certification. It enhances skills in solving complex ML challenges using Google’s cutting-edge technologies.
- Requirements / Prerequisites
- Strong Python Proficiency: Essential for hands-on labs, including core libraries and object-oriented programming.
- Fundamental ML Concepts: Basic understanding of supervised/unsupervised learning, regression, classification, and common evaluation metrics.
- Basic Math for ML: A conceptual grasp of linear algebra and statistics is beneficial for comprehending algorithm mechanics.
- Cloud Basics: Familiarity with command-line tools and conceptual cloud computing principles is helpful, though not strictly required for GCP.
- Skills Covered / Tools Used
- Advanced ML Architectures: Design and implement deep learning models (CNNs, RNNs, Transformers) using TensorFlow and Keras.
- Google Cloud AI/ML Suite: Expertise in Vertex AI (Workbench, Training, Endpoints, Pipelines), BigQuery ML, Dataflow, and Cloud Storage for comprehensive ML solution development.
- MLOps & CI/CD: Implement continuous integration/delivery, model versioning, monitoring, and automated retraining for production ML systems.
- Data Engineering for ML: Techniques for large-scale data ingestion, transformation, and feature engineering using GCP tools.
- Model Deployment & Scaling: Strategies for deploying, managing, and scaling ML models for inference via Vertex AI Endpoints, Cloud Run, and containerization.
- Responsible AI Practices: Understand and apply principles of fairness, interpretability, and bias detection in ML model design and evaluation.
- Benefits / Outcomes
- Google Professional Certification: Successfully prepare for and achieve the highly regarded Google Certified Professional Machine Learning Engineer credential.
- Career Advancement: Position yourself for high-demand roles like ML Engineer, AI Architect, or Senior Data Scientist, significantly boosting employability.
- End-to-End ML Solutions: Gain practical ability to design, build, deploy, and operationalize robust, scalable, and ethical ML solutions on Google Cloud Platform.
- GCP AI/ML Mastery: Develop deep proficiency in leveraging Google Cloud’s extensive AI/ML services to solve complex real-world business challenges.
- MLOps Expertise: Acquire critical skills in implementing MLOps best practices, ensuring reliability, maintainability, and continuous improvement of deployed ML models.
- Problem-Solving Confidence: Build expertise to tackle intricate ML engineering problems, from data management to model optimization and responsible AI.
- PROS
- Official Certification Alignment: Directly prepares you for the Google Professional Machine Learning Engineer exam with relevant, updated content.
- Practical & Comprehensive: Covers the entire ML lifecycle on GCP, emphasizing hands-on application and best practices.
- Extensive Practice Material: Includes 137 questions with detailed explanations, crucial for solidifying understanding and exam readiness.
- Highly Rated & Popular: Boasts a 4.33/5 rating from over 41,920 students, indicating high quality and student satisfaction.
- Globally Recognized Credential: Offers the opportunity to earn an industry-leading certification, enhancing career prospects significantly.
- CONS
- Requires substantial dedication and a strong technical foundation to fully grasp the breadth and depth of advanced ML engineering concepts and practical applications.
Learning Tracks: English,IT & Software,IT Certifications
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