What will you learn in this Machine Learning on Google Cloud Specialization Course
Build, train, and deploy machine learning models using Vertex AI AutoML and BigQuery ML without extensive coding knowledge.
Implement custom machine learning models using Keras and TensorFlow 2.x.
Understand and apply best practices for machine learning in enterprise environments.
Perform exploratory data analysis and improve data quality for machine learning projects.
Program Overview
Introduction to AI and Machine Learning on Google Cloud
⏳ 9 hours
Learn about Google’s AI and ML offerings and how to build ML models using Vertex AI.
Build, Train, and Deploy ML Models with Keras on Google Cloud
⏳ 13 hours
Design and build TensorFlow input data pipelines and deploy ML models at scale with Vertex AI.
Feature Engineering
⏳ 8 hours
Perform feature engineering using BigQuery ML, Keras, and TensorFlow, and explore features with Dataflow and Dataprep.
Machine Learning in the Enterprise
⏳ 8 hours
Identify and use core technologies required to support effective MLOps and implement reliable training and inference workflows.
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Job Outlook
Equips learners with practical skills for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
Provides hands-on experience in deploying and maintaining ML systems in production environments using Google Cloud technologies.
Enhances qualifications for positions requiring expertise in machine learning and cloud-based solutions.
Specification: Machine Learning on Google Cloud Specialization
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FAQs
- Basic understanding of Python and programming concepts is recommended.
- Familiarity with statistics and linear algebra helps but is not mandatory.
- Introductory ML concepts are introduced progressively.
- Prior experience with cloud platforms is helpful but not required.
- The course is suitable for beginners looking to apply ML on Google Cloud.
- Provides scalable computing resources for large datasets.
- Simplifies deployment and management of ML models.
- Offers integrated services like BigQuery, AI Platform, and Vertex AI.
- Supports collaboration and versioning in team environments.
- Reduces hardware dependency and setup complexity.
- Supervised models like regression and classification.
- Unsupervised models such as clustering and dimensionality reduction.
- Neural networks for structured and unstructured data.
- Model evaluation, hyperparameter tuning, and optimization.
- Deployment of trained models on Google Cloud infrastructure.
- Prepares for roles like ML engineer and AI specialist.
- Provides practical skills for cloud-based deployment of models.
- Enhances employability in companies using Google Cloud.
- Supports portfolio development with real-world ML projects.
- Lays the foundation for advanced AI and data science certifications.
- Includes interactive labs and assignments on Google Cloud.
- Encourages building end-to-end ML pipelines.
- Real-world datasets are used for practice.
- Offers step-by-step guidance for model deployment.
- Provides experience in production-level ML workflows.
