Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations
β±οΈ Length: 16.5 total hours
β 4.08/5 rating
π₯ 40,232 students
π July 2023 update
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Course Overview
- This course is your definitive pathway to becoming a Google Certified Professional Machine Learning Engineer, translating advanced ML knowledge into practical, scalable solutions within the Google Cloud ecosystem.
- Designed for end-to-end ML project immersion, it covers everything from data ingestion to continuous model deployment and monitoring using Google’s cutting-edge AI/ML services.
- Master building robust, production-ready machine learning systems for real-world complexities, aligning perfectly with official Google Cloud certification objectives.
- Gain expertise in harnessing TensorFlow and advanced ML algorithms, tailored for efficient execution on Google Cloud Platform, ensuring highly performant solutions.
- The curriculum emphasizes practical application through scenarios relevant to the certification exam, making you proficient across the entire ML landscape on GCP.
- Benefit from an interactive experience, bolstered by numerous practice questions and detailed explanations, providing solid confidence for the professional certification exam.
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Requirements / Prerequisites
- Foundational understanding of core machine learning concepts, including model types, training processes, and evaluation metrics.
- Solid proficiency in Python programming, encompassing data structures, object-oriented principles, and common ML libraries.
- Familiarity with fundamental statistical concepts and linear algebra, which underpin many ML algorithms.
- Basic exposure to cloud computing concepts, ideally with some hands-on experience on any major cloud platform.
- Comfort working with command-line interfaces and navigating basic Linux environments, essential for cloud interaction.
- A commitment to active learning and practical application, as the course heavily emphasizes hands-on problem-solving.
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Skills Covered / Tools Used
- TensorFlow and Keras: Deep expertise in building, training, and deploying advanced deep learning models.
- Google Cloud AI/ML Services (Vertex AI): Comprehensive mastery of Google’s unified ML platform for managing the entire ML lifecycle.
- Vertex AI Workbench (Managed Notebooks): Skill in utilizing integrated JupyterLab environments for interactive ML development and experimentation.
- BigQuery ML: Competence in performing machine learning directly within Google Cloud’s data warehouse for faster insights.
- Dataflow and Dataproc: Advanced techniques for building scalable data pipelines and processing massive datasets for ML readiness.
- Cloud Storage and Bigtable: Strategic use of cloud storage solutions for data persistence, model artifacts, and large-scale feature stores.
- Container Registry and Artifact Registry: Best practices for containerizing ML models and managing their versions for seamless deployment.
- Vertex AI Pipelines (Kubeflow Pipelines): Expertise in orchestrating automated, reproducible, and scalable MLOps workflows.
- Model Monitoring and Explainability (XAI): Implementing tools and strategies to ensure model performance, detect drift, and interpret predictions effectively.
- Hyperparameter Tuning (Vertex AI Vizier): Advanced optimization techniques for achieving optimal model performance efficiently.
- Distributed Training Strategies: Understanding and implementing techniques for training large models on massive datasets across multiple machines.
- Cost Optimization and Resource Management on GCP: Skills in designing cost-effective and resource-efficient ML solutions.
- MLOps Principles: Application of DevOps principles to machine learning, focusing on CI/CD for ML.
- Python Ecosystem: Proficient use of libraries like NumPy, Pandas, and Scikit-learn for data manipulation and analysis.
- Security and Compliance in ML: Understanding and implementing best practices for securing ML data and models on GCP.
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Benefits / Outcomes
- Achieve the esteemed Google Cloud Professional Machine Learning Engineer certification, validating your expertise globally.
- Become proficient in designing, developing, and deploying robust, scalable, and secure ML solutions within the Google Cloud ecosystem.
- Significantly enhance your career prospects and market value in the rapidly evolving fields of AI, machine learning, and cloud engineering.
- Gain a holistic understanding of the MLOps lifecycle, enabling effective building and management of production-grade ML pipelines.
- Master an extensive suite of Google Cloud AI/ML services, equipping you with tools to tackle complex data science challenges.
- Develop confidence to architect and implement end-to-end ML projects, from data exploration to continuous model improvement.
- Contribute to ethical and responsible AI practices by understanding model explainability and governance.
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PROS
- Exceptional preparation for the Google Cloud Professional Machine Learning Engineer exam, directly mapping to its objectives.
- Highly practical curriculum emphasizing hands-on application and real-world problem-solving using GCP.
- Includes extensive practice questions (137 Q&A) with detailed explanations, invaluable for solidifying understanding and exam readiness.
- Strong social proof with a high rating (4.08/5) and a large student base (40,232), indicating effectiveness and popularity.
- Content is regularly updated (July 2023 update), ensuring relevance with the latest Google Cloud AI/ML service offerings.
- Offers a comprehensive deep dive into the Google Cloud AI/ML stack, fostering specialized expertise.
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CONS
- Assumes a foundational understanding of machine learning and Python; beginners without these prerequisites might find the pace challenging.
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