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.
Get certificate
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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Machine Learning Specialization Course – Gain a comprehensive understanding of machine learning algorithms, model building, and evaluation techniques.
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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.
