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Generative AI Foundations for Cloud

Saravanan Dhandapani

Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.

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Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.

Being the front runner when it comes to cloud infrastructure, AWS has cutting edge services when it comes to machine learning. In this course, Modeling with AWS Machine Learning, you’ll learn to convert your data to an optimal model leveraging AWS SageMaker. First, you’ll explore supervised and unsupervised learning algorithms that are built-in to your AWS account and learn how to apply them to a specific business problem. Next, you’ll discover deep learning neural networks architecture and the built-in algorithms provided by AWS that cater specifically to computer vision and language processing domain. Finally, you’ll learn how to train a model on a SageMaker notebook, evaluate the model against the objective metric, and fine-tune the hyperparameters and arrive at an optimally performing model. When you’re finished with this course, you’ll have the skills and knowledge of all the AWS built-in algorithms and train, evaluate, and tune your models that are needed to master AWS SageMaker and clear AWS Machine Learning Specialty certification exam.

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What's inside

Syllabus

Course Overview
ML Foundation and Supervised Learning Algorithms
Deep Learning Foundation and Algorithms
Train ML Models
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Evaluate ML Models
Tune ML Models

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops skills in all the different ML algorithms provided by AWS, which is highly relevant in industry
Taught by Saravanan Dhandapani, who is recognized for their work in Machine Learning
Covers different ML algorithms, including supervised learning, unsupervised learning, deep learning, and neural networks
Provides hands-on experience in training, evaluating, and tuning ML models
Prepares learners for the AWS Machine Learning Specialty certification exam

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Activities

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Career center

Learners who complete Generative AI Foundations for Cloud will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer trains models and builds prototypes to apply machine learning to new problems. This course can help you build the foundational knowledge to become a successful Machine Learning Engineer by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Machine Learning Engineers.
Data Scientist
Data Scientists use machine learning to solve business problems. This course can help build the foundation you need to become a Data Scientist by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Data Scientists.
Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and techniques. This course can help you build the foundational knowledge to become a Machine Learning Researcher by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Machine Learning Researchers.
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and maintains AI systems. This course can help you build the foundational knowledge to become an Artificial Intelligence Engineer by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Artificial Intelligence Engineers.
Natural Language Processing Engineer
A Natural Language Processing Engineer develops and maintains natural language processing systems. This course can help you build the foundational knowledge to become a Natural Language Processing Engineer by teaching you the built-in natural language processing algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Natural Language Processing Engineers.
Computer Vision Engineer
A Computer Vision Engineer develops and maintains computer vision systems. This course can help you build the foundational knowledge to become a Computer Vision Engineer by teaching you the built-in computer vision algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Computer Vision Engineers.
Data Analyst
A Data Analyst analyzes data to find patterns and trends. This course can help you build the foundational knowledge to become a Data Analyst by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Data Analysts.
Big Data Engineer
A Big Data Engineer designs and builds big data systems. This course can help build a foundation for a career as a Big Data Engineer by teaching you the built-in algorithms available to use with AWS, as well as how to train and evaluate models. Additionally, you'll learn how to fine-tune hyperparameters to optimize performance, which is an essential skill for Big Data Engineers.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course can help build a foundation for a career as a Software Engineer by teaching you how to use the built-in AWS machine learning algorithms to create and deploy machine learning models as part of software systems.
Cloud Architect
A Cloud Architect designs and builds cloud computing solutions. This course can help build a foundation for a career as a Cloud Architect by teaching you how to use the built-in AWS machine learning algorithms to create and deploy machine learning models in the cloud.
IT Architect
An IT Architect designs and builds IT systems. This course can help build a foundation for a career as an IT Architect by teaching you how to use the built-in AWS machine learning algorithms to create and deploy machine learning models as part of IT systems.
Business Analyst
A Business Analyst analyzes business needs and develops solutions. This course can help build a foundation for a career as a Business Analyst by teaching you how to use the built-in AWS machine learning algorithms to analyze data and develop business solutions.
IT Manager
An IT Manager plans and manages IT systems. This course can help build a foundation for a career as an IT Manager by teaching you how to use the built-in AWS machine learning algorithms to optimize IT systems.
Product Manager
A Product Manager plans and manages the development of products. This course can help build a foundation for a career as a Product Manager by teaching you how to use the built-in AWS machine learning algorithms to improve product development.
Entrepreneur
An Entrepreneur starts and runs a business. This course can help build a foundation for a career as an Entrepreneur by teaching you how to use the built-in AWS machine learning algorithms to improve business operations.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI Foundations for Cloud.
Provides a comprehensive overview of deep learning concepts and techniques, particularly useful for understanding the deep learning algorithms covered in the course.
A practical guide to applying machine learning algorithms to real-world problems, providing valuable insights into the implementation and evaluation of models.
Provides a theoretical foundation for machine learning, covering probabilistic models and algorithms, offering a deeper understanding of the underlying principles.
Provides a practical introduction to natural language processing techniques using Python, covering topics relevant to the course's deep learning algorithms for language processing.
Provides a comprehensive overview of computer vision algorithms and applications, offering a deeper understanding of the deep learning algorithms for computer vision covered in the course.
Provides a detailed overview of generative adversarial networks (GANs), a type of generative model that is gaining increasing attention in the field.
A comprehensive textbook covering pattern recognition and machine learning, providing a broad overview of the field beyond the specific topics covered in the course.

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