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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/0854aw/generative
What's inside
Syllabus
Course Overview
ML Foundation and Supervised Learning Algorithms
Deep Learning Foundation and Algorithms
Train ML Models
Read more
Syllabus
Course Overview
ML Foundation and Supervised Learning Algorithms
Deep Learning Foundation and Algorithms
Train ML Models
Read more
Evaluate ML Models
Tune ML Models
Good to know
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
Save this course
Save Generative AI Foundations for Cloud to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Generative AI Foundations for Cloud with these
activities:
Review Supervised Learning Algorithms
Show steps
Review basic concepts of supervised learning. This will improve your foundational knowledge which will aid you when learning more advanced topics throughout the course.
Browse courses on
Supervised Learning
Show steps
-
Read through provided materials covering supervised learning
-
Complete practice problems focusing on supervised learning algorithms
-
Complete practice problems implementing supervised learning algorithms
Complete the AWS SageMaker Getting Started tutorial
Show steps
Develop a solid foundation in AWS SageMaker.
Browse courses on
AWS SageMaker
Show steps
-
Follow the step-by-step instructions in the tutorial.
-
Train and deploy a simple machine learning model.
Attend AWS hosted workshops
Show steps
This will provide you the opportunity to ask questions to experts, build your network, and get hands-on experience with the core concepts and key topics of this course.
Show steps
-
Visit https://aws.amazon.com/events/ to find a workshop near you.
-
Select a workshop that focuses on AWS ML or SageMaker.
-
Register for the workshop.
-
Complete any pre-work or prerequisites for the workshop.
-
Attend the workshop.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice building and training ML models using AWS SageMaker
Show steps
This will allow you to apply the theories and techniques learned in this course to practical, hands-on scenarios.
Show steps
-
Set up an AWS account and create an Amazon SageMaker notebook instance.
-
Follow the tutorials in the AWS SageMaker documentation to build and train a simple ML model.
-
Experiment with different algorithms, parameters, and datasets.
-
Evaluate the performance of your models and identify areas for improvement.
Create a simple machine learning model using AWS SageMaker
Show steps
Gain hands-on experience building and deploying ML models.
Browse courses on
Machine Learning Projects
Show steps
-
Gather your data and prepare it for training.
-
Choose an appropriate ML algorithm for your task.
-
Train your model using AWS SageMaker.
-
Evaluate the performance of your model.
-
Deploy your model to a production environment.
Write a blog post about a specific AWS SageMaker feature
Show steps
Enhance your understanding by explaining a concept to others.
Browse courses on
AWS SageMaker
Show steps
-
Choose a feature of AWS SageMaker to focus on.
-
Research the feature and gather information.
-
Write a blog post that explains the feature clearly and concisely.
-
Publish your blog post and share it with others.
Review Supervised Learning Algorithms
Show steps
Review basic concepts of supervised learning. This will improve your foundational knowledge which will aid you when learning more advanced topics throughout the course.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Read through provided materials covering supervised learning
- Complete practice problems focusing on supervised learning algorithms
- Complete practice problems implementing supervised learning algorithms
Complete the AWS SageMaker Getting Started tutorial
Show steps
Develop a solid foundation in AWS SageMaker.
Browse courses on
AWS SageMaker
Show steps
Show steps
Show steps
- Follow the step-by-step instructions in the tutorial.
- Train and deploy a simple machine learning model.
Attend AWS hosted workshops
Show steps
This will provide you the opportunity to ask questions to experts, build your network, and get hands-on experience with the core concepts and key topics of this course.
Show steps
Show steps
Show steps
- Visit https://aws.amazon.com/events/ to find a workshop near you.
- Select a workshop that focuses on AWS ML or SageMaker.
- Register for the workshop.
- Complete any pre-work or prerequisites for the workshop.
- Attend the workshop.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice building and training ML models using AWS SageMaker
Show steps
This will allow you to apply the theories and techniques learned in this course to practical, hands-on scenarios.
Show steps
Show steps
Show steps
- Set up an AWS account and create an Amazon SageMaker notebook instance.
- Follow the tutorials in the AWS SageMaker documentation to build and train a simple ML model.
- Experiment with different algorithms, parameters, and datasets.
- Evaluate the performance of your models and identify areas for improvement.
Create a simple machine learning model using AWS SageMaker
Show steps
Gain hands-on experience building and deploying ML models.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Gather your data and prepare it for training.
- Choose an appropriate ML algorithm for your task.
- Train your model using AWS SageMaker.
- Evaluate the performance of your model.
- Deploy your model to a production environment.
Write a blog post about a specific AWS SageMaker feature
Show steps
Enhance your understanding by explaining a concept to others.
Browse courses on
AWS SageMaker
Show steps
Show steps
Show steps
- Choose a feature of AWS SageMaker to focus on.
- Research the feature and gather information.
- Write a blog post that explains the feature clearly and concisely.
- Publish your blog post and share it with others.
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
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 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
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
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
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
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
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
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
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
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
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
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
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
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
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.
For more career information including salaries, visit:
OpenCourser.com/course/0854aw/generative
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.
A comprehensive reference book covering statistical learning methods, providing a deeper understanding of the foundations of machine learning algorithms.
Provides broad coverage of ML algorithms and their applications in various domains, making it a valuable resource for exploring use cases beyond those covered in the course.
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.
Save
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.
Save
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.
Save
A comprehensive textbook covering data mining techniques, providing a solid foundation for understanding the algorithms used in the course.
Useful reference book for learning more about Machine Learning with R especially if the student is unfamiliar with the language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0854aw/generative
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Time
72 hours
Level
Advanced
Via
Pluralsight
Instructor
Saravanan Dhandapani
Language
English
Good to know
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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