Machine Learning on Google Cloud Course Overview

Target Audience for Machine Learning on Google Cloud

The Machine Learning on Google Cloud course equips learners with hands-on ML skills using Google's cutting-edge tools and best practices.

Target audience for the Machine Learning on Google Cloud course includes:

• Data Scientists interested in applying ML on the cloud
• Data Analysts looking to upgrade to machine learning roles
• IT Professionals seeking to understand ML applications on GCP
• Machine Learning Engineers aiming to refine their skills with Google Cloud tools
• Software Developers aspiring to integrate ML into applications
• Cloud Solution Architects designing ML solutions on Google Cloud
• AI Specialists seeking to leverage Google Cloud's AI Platform
• Business Intelligence Professionals exploring ML use cases and data strategy
• DevOps Engineers focused on deploying ML models at scale
• System Administrators managing ML model deployments on cloud infrastructure
• Product Managers overseeing ML product development
• Tech-savvy Managers seeking to guide teams through ML projects on GCP
• Students and Academics pursuing knowledge in cloud-based machine learning
• Research Scientists using ML for innovative research projects
• Technical Sales Professionals consulting on GCP machine learning tools and services

Learning Objectives - What you will Learn in this Machine Learning on Google Cloud?

Brief Introduction

This Machine Learning on Google Cloud course equips students with practical skills and insights for applying machine learning to solve real-world problems using Google Cloud's powerful tools and services.

Learning Objectives and Outcomes

• Develop a robust data strategy incorporating machine learning, recognizing and mitigating biases in ML models.
• Reimagine traditional use cases through the lens of machine learning to innovate and enhance business processes.
• Utilize Google Cloud Platform (GCP) tools for effective machine learning, learning from Google's best practices to evade common pitfalls.
• Conduct data science tasks in collaborative online notebooks, leveraging pre-trained models on the Cloud AI Platform.
• Improve data quality and perform exploratory analysis to build, train, and evaluate supervised learning models.
• Define and implement strategies to optimize model performance, including loss functions and metrics, while addressing typical machine learning challenges.
• Create, train, and manage TensorFlow 2.x and Keras models, understanding the tf.data library for data manipulation.
• Master feature engineering, from creating feature crosses to preprocessing with Cloud Dataflow and Cloud Dataprep, and using TensorFlow for feature transformation.
• Optimize models with hyperparameter tuning, experiment with neural network architectures, and enhance features with embedding layers for better performance.
• Deploy and scale machine learning models efficiently with the Cloud AI Platform, ensuring models are production-ready.