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Welcome to the ultimate online learning experience with our comprehensive AWS SageMaker Bootcamp course on Udemy.

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Welcome to the ultimate online learning experience with our comprehensive AWS SageMaker Bootcamp course on Udemy.

This meticulously designed course is your gateway to mastering AWS SageMaker, a powerful cloud machine learning platform that allows developers to build, train, and deploy machine learning models quickly.

Embark on a learning journey starting with an introduction to the course, where we cover frequently asked questions, provide essential course downloads, and give you a detailed curriculum overview. We'll also guide you through setting up your AWS console, ensuring you're prepared to dive deep into the world of AWS SageMaker.

Delve into the heart of AWS SageMaker with an in-depth exploration of what SageMaker is and how to navigate its console. Learn about SageMaker domains and how to create your own, setting the stage for practical, hands-on learning.

Transform your theoretical knowledge into practical expertise with our section on SageMaker Notebook Instances. Discover the power of SageMaker Notebooks, learn how to utilize them effectively, and engage in a project that puts your newly acquired knowledge to the test.

Advance your skills further with the Amazon SageMaker Python SDK. This section introduces you to the SageMaker Python Library, data processing techniques, and leads you through a project that leverages SageMaker's auto ML capabilities.

Explore the possibilities with SageMaker Canvas, starting with an introduction to Auto ML. Discover the Canvas Overview, and dive into data import, data wrangling, preparation, and inference using ready-to-use models. Learn about custom model creation, model evaluation, and inference to broaden your machine learning capabilities.

This course is designed for learners of all levels interested in AWS SageMaker, from beginners to advanced users looking to refine their skills. Whether you're aiming to advance your career, embark on new machine learning projects, or simply passionate about cloud computing and machine learning, this course is the perfect stepping stone to achieving your goals. Join us on this exciting journey to mastering AWS SageMaker.

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops skills useful for personal growth and development
Emphasizes hands-on learning through projects
Led by instructors not recognized for their work with AWS SageMaker
Provides a foundation for learners new to AWS SageMaker
Teaches skills highly relevant to indusry
Provides a strong foundation for intermediate learners

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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 Amazon SageMaker with these activities:
Follow a tutorial on AWS SageMaker
Following a tutorial can help you to learn about specific features and capabilities of AWS SageMaker.
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  • Find a tutorial on the AWS website or other online resources.
  • Follow the steps in the tutorial.
  • Experiment with the code and settings in the tutorial.
Complete practice exercises on AWS SageMaker
Regular practice will help you to improve your skills and knowledge of AWS SageMaker.
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  • Find practice exercises on the AWS website or other online resources.
  • Set aside time each week to complete the exercises.
  • Review your results and identify areas for improvement.
Build a simple machine learning model
Hands-on experience with building a machine learning model will help you understand the concepts covered in the course.
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  • Choose a simple dataset and machine learning task.
  • Use AWS SageMaker to create a notebook instance.
  • Train and evaluate your model using the SageMaker Python SDK.
  • Deploy your model as an endpoint.
Four other activities
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Show all seven activities
Write a blog post about AWS SageMaker
Explaining concepts to others can help you to solidify your own understanding.
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  • Choose a topic related to AWS SageMaker that you are interested in.
  • Research the topic and gather information.
  • Write a draft of your blog post.
  • Edit and revise your post.
  • Publish your post on a platform like Medium or your own website.
Create a presentation on AWS SageMaker for your team
Preparing and delivering a presentation will help you to master the key concepts of AWS SageMaker.
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Show steps
  • Choose a topic related to AWS SageMaker that you are interested in.
  • Research the topic and gather information.
  • Create a presentation outline.
  • Develop your presentation slides.
  • Practice your presentation.
  • Deliver your presentation to your team.
Attend an AWS meetup or conference
Networking with other professionals can help you to learn about new trends and best practices in AWS SageMaker.
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  • Find an AWS meetup or conference in your area.
  • Register for the event.
  • Attend the event and network with other attendees.
Attend an AWS SageMaker workshop
Workshops can provide you with in-depth training on specific topics related to AWS SageMaker.
Browse courses on AWS SageMaker
Show steps
  • Find an AWS SageMaker workshop in your area.
  • Register for the workshop.
  • Attend the workshop and participate in the activities.

Career center

Learners who complete Amazon SageMaker will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a comprehensive overview of AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be invaluable for Machine Learning Engineers who want to use SageMaker to build and deploy their own machine learning models.
Data Engineer
Data Engineers are responsible for designing, building, and maintaining the infrastructure and processes that allow organizations to collect, store, and analyze data. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Data Engineers who want to use machine learning to improve their data pipelines.
Data Scientist
Data Scientists use machine learning to solve business problems. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Data Scientists who want to use machine learning to solve business problems.
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Machine Learning Researchers who want to use SageMaker to develop and test new machine learning algorithms and techniques.
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Data Analysts who want to use machine learning to improve their data analysis.
Cloud Architect
Cloud Architects design and manage cloud computing solutions. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Cloud Architects who want to build machine learning solutions in the cloud.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to solve business problems. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Quantitative Analysts who want to use machine learning to solve business problems.
Statistician
Statisticians collect, analyze, and interpret data to help businesses make better decisions. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Statisticians who want to use machine learning to improve their data analysis.
Product Manager
Product Managers are responsible for the development and management of products. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Product Managers who want to build machine learning products.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Operations Research Analysts who want to use machine learning to solve business problems.
Business Analyst
Business Analysts analyze business needs and develop solutions to improve business processes. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Business Analysts who want to use machine learning to improve business processes.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Software Engineers who want to build machine learning applications.
Financial Analyst
Financial Analysts analyze financial data to make investment decisions. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Financial Analysts who want to use machine learning to make better investment decisions.
Marketing Analyst
Marketing Analysts analyze marketing data to understand customer behavior and improve marketing campaigns. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Marketing Analysts who want to use machine learning to improve their marketing campaigns.
Risk Analyst
Risk Analysts analyze risks and develop strategies to mitigate those risks. This course provides a foundation in AWS SageMaker, a cloud machine learning platform that can be used to build and deploy machine learning models. This knowledge can be valuable for Risk Analysts who want to use machine learning to identify and mitigate risks.

Reading list

We've selected 14 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 Amazon SageMaker.
Is written by a former AWS employee and covers topics such as architecture, data preparation, and deployment. It comprehensive guide that can be used as a reference or as a textbook.
Practical guide to machine learning with R. It covers topics such as data preparation, model training, and evaluation.
Comprehensive guide to deep learning with R. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It provides a solid foundation for understanding the concepts of deep learning.
An in-depth guide to deep learning theory and techniques. Useful for learners interested in exploring advanced machine learning models in AWS SageMaker.
Comprehensive guide to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It provides a solid foundation for understanding the concepts of deep learning.
Comprehensive guide to machine learning with Python. It covers topics such as data preparation, model training, and evaluation.
Practical guide to machine learning for hackers. It covers topics such as data preparation, model training, and evaluation.
A practical guide to data analysis using Python. Complements the course by providing additional resources for working with data in AWS SageMaker.
Gentle introduction to machine learning with Python. It covers topics such as data preparation, model training, and evaluation.
Comprehensive introduction to machine learning. It covers topics such as linear regression, logistic regression, and neural networks. It provides a solid foundation for understanding the concepts of machine learning.
Covers natural language processing (NLP) concepts and techniques. Useful for learners interested in exploring NLP applications in AWS SageMaker.

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