What will you learn in this Machine Learning on Google Cloud Specialization Course
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Build, train, and deploy machine learning models using Vertex AI AutoML and BigQuery ML without extensive coding knowledge.
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Implement custom machine learning models using Keras and TensorFlow 2.x.
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Understand and apply best practices for machine learning in enterprise environments.
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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
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Equips learners with practical skills for roles such as Machine Learning Engineer, Data Scientist, and AI Specialist.
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Provides hands-on experience in deploying and maintaining ML systems in production environments using Google Cloud technologies.
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Enhances qualifications for positions requiring expertise in machine learning and cloud-based solutions.
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Applied Machine Learning in Python Course – Build practical skills in implementing machine learning models using Python for hands-on data analysis and prediction tasks.
Related Reading
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What Is Data Management? – Understand how effective data management is essential for training, deploying, and maintaining machine learning models on cloud platforms.
