OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

Modeling in AWS

Whizlabs Instructor

Modeling in AWS is the third course in the AWS Certified Machine Learning Specialty specialization. The major focus of this course is to train Machine learning Models by analyzing Modeling concepts in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:30 Hours- 2:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.

Read more

Modeling in AWS is the third course in the AWS Certified Machine Learning Specialty specialization. The major focus of this course is to train Machine learning Models by analyzing Modeling concepts in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:30 Hours- 2:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.

Module 1: Modeling and Training Machine Learning Models in AWS

Module 2: Machine Learning Models: Performance evaluation and Tuning

By the end of this course, Learners will be able to :

1. Analyze Modeling Concepts and train Machine Learning Models

2. Examine performance of machine learning models

3. Implement automatic model tuning by training a model

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

What's inside

Syllabus

Modeling and Training Machine Learning Models in AWS
Welcome to Week 1 of Modeling in AWS course. We’ll Introduce Modeling Concepts in Machine Learning in the beginning. We’ll also describe the concept of Training machine learning models. The week will end with a demonstration on how to train machine learning models.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches concepts used for modeling machine learning in the real-world
Strong match for learners who are looking to broaden their understanding of the conceptual underpinnings of machine learning models
Taught by instructors from Whizlabs, who are recognized for their work in training machine learning models
Provides hands-on labs and interactive materials, which helps to ensure that learning is practical
Focuses on the practical application of machine learning models, which could help learners develop skills that are valuable to employers
Part of a specialization, which may be appealing to learners seeking a comprehensive overview of machine learning modeling

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Modeling in aws: strong concepts, outdated labs

According to learners, 'Modeling in AWS' offers a strong conceptual foundation for machine learning on AWS, proving highly beneficial for those pursuing the AWS Machine Learning Specialty certification. Many appreciate the clear explanations and practical demonstrations for topics like automatic model tuning. However, a significant number of students report that the hands-on labs are outdated, leading to frustration with environment setup and debugging. While the course provides useful quizzes, the inconsistency in practical exercises can sometimes create a challenging learning experience, especially for those seeking seamless implementation.
Provides clear and concise explanations of core ML modeling concepts.
"This course is incredibly well-structured and the instructor's explanations were clear and concise, making complex topics easy to grasp."
"The theory is well-explained and the concepts are presented in a very understandable manner."
"I found the content relevant and the theoretical understanding provided a solid base for AWS ML."
Highly relevant and beneficial for the AWS ML Specialty certification.
"Highly recommend for anyone pursuing the AWS ML specialty cert. It covers key areas directly relevant to the exam."
"This course provides a solid foundation for the certification exam. I feel much more prepared after taking it."
"I found it very practical and to the point for what I needed for the specialty certification."
May lack depth for experienced learners; pacing can feel inconsistent.
"Disappointed with the lack of depth. It's too high-level for someone with prior ML experience."
"Some parts felt a bit rushed, and I wish there were more in-depth examples for model evaluation beyond the basics."
"Sometimes the pace felt a bit slow initially, then too fast, which made it hard to follow consistently."
Hands-on labs are frequently reported as outdated and frustrating.
"The theory is well-explained, but the practical labs are outdated. I struggled with some of the AWS services mentioned, as the UI has changed."
"Completely outdated course materials, especially the lab instructions. I spent more time debugging environment setup than learning."
"Just be aware that you might need to troubleshoot lab issues due to AWS updates and discrepancies with the course content."

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 Modeling in AWS with these activities:
Review Python Fundamentals
Ensure you have a strong foundation in Python, as it is the primary language used in this course.
Browse courses on Python Programming
Show steps
  • Review Python syntax and data structures
  • Practice writing simple Python programs
Review AWS Cloud Services
Familiarize yourself with key AWS services, ensuring you have a solid understanding of the cloud platform.
Browse courses on AWS Services
Show steps
  • Review AWS core services, such as EC2, S3, and RDS
  • Explore AWS regions and availability zones
Review Machine Learning Concepts
Refresh your understanding of foundational machine learning concepts before diving into modeling.
Browse courses on Machine Learning Fundamentals
Show steps
  • Review supervised and unsupervised learning algorithms
  • Understand model evaluation metrics and techniques
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Review statistics and probability
Review fundamental statistics and probability concepts to strengthen your foundational understanding for this course.
Browse courses on Statistics
Show steps
  • Study notes or textbooks
  • Solve practice problems
  • Attend a refresher workshop or tutorial
Join a Study Group or Discussion Forum
Engage with peers and instructors in a study group or discussion forum to clarify concepts and share knowledge.
Show steps
  • Join an online discussion forum or study group
  • Participate in discussions, ask questions, and share your insights
Create a personalized study guide
Organize and consolidate course materials to enhance your understanding and facilitate effective revision.
Browse courses on Study Guide
Show steps
  • Gather notes, slides, and assignments from the course
  • Review and summarize key concepts and topics
  • Include additional resources such as articles or videos
Practice training machine learning models
Engage in hands-on practice to solidify your understanding of training machine learning models using AWS.
Browse courses on AWS SDK
Show steps
  • Complete the hands-on exercises provided in the course modules
  • Work on practice problems or tutorials
  • Participate in online coding challenges or competitions
Read AWS Blogs and Documentation
Supplement your understanding of modeling concepts by reviewing official AWS documentation and blog posts.
Browse courses on Machine Learning Models
Show steps
  • Visit the AWS Machine Learning blog
  • Explore the AWS documentation for Modeling in AWS
Join a study group or discussion forum
Engage with peers to clarify concepts, share knowledge, and provide support in your learning journey.
Show steps
  • Find a study group or discussion forum related to the course
  • Participate actively in discussions and ask questions
  • Offer help and support to other students
Google Cloud Architecture Tutorials
These tutorials provide a structured approach to learning the concepts and techniques of modeling in AWS and can help you reinforce your understanding of the course material.
Browse courses on Machine Learning Models
Show steps
  • Select tutorials that align with your learning goals.
  • Follow the step-by-step instructions carefully.
  • Apply the concepts in practice exercises.
AWS Model Evaluation Study Group
Collaborating with peers in a study group can provide diverse perspectives, enhance your understanding of model evaluation strategies, and foster a supportive learning environment.
Browse courses on Model Evaluation
Show steps
  • Find or invite peers to join a study group.
  • Establish regular meeting times and discussion topics.
  • Engage actively in discussions, share insights, and ask clarifying questions.
AWS Model Training Practice Drills
Regular practice through drills can help you refine your model training skills, identify areas for improvement, and boost your confidence in working with models in AWS.
Browse courses on Model Training
Show steps
  • Find reliable practice questions and exercises online or in resources provided by AWS.
  • Allocate time for solving practice problems.
  • Analyze your performance to identify strengths and weaknesses.
Attend an AWS Machine Learning Workshop
Supplement your learning by attending an AWS Machine Learning workshop led by industry experts.
Show steps
  • Identify and register for an AWS Machine Learning workshop
  • Attend the workshop and actively participate in hands-on exercises
Build a machine learning model
Apply your knowledge by building a machine learning model using AWS, allowing you to practice the entire workflow.
Browse courses on Machine Learning Model
Show steps
  • Identify a problem or dataset to work on
  • Design and implement a machine learning model
  • Train and evaluate the model
  • Deploy the model and monitor its performance
AWS Model Tuning Project
Creating your own model provides hands-on experience with the entire model lifecycle, enabling you to apply your knowledge of tuning and evaluation techniques in a practical setting.
Browse courses on Model Tuning
Show steps
  • Define the problem you want to solve and gather relevant data.
  • Select an appropriate machine learning model for your project.
  • Implement the model and explore different tuning parameters.
  • Evaluate the performance of your model and iterate to improve its accuracy.
  • Deploy the tuned model and monitor its performance.
Build a Personal Project
Deepen your knowledge by applying modeling concepts to a personal project of your choice.
Show steps
  • Identify a problem or opportunity that machine learning can address
  • Design and develop a machine learning model to solve the problem
  • Train and evaluate your model using AWS services
  • Deploy and monitor your model in a real-world scenario

Career center

Learners who complete Modeling in AWS will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data scientists build and maintain models to uncover patterns and develop data-driven insights for a variety of organizations. During the course of their work, they need to be able to create machine learning models for real-world applications. The Modeling in AWS course can help learners build the skills to do this and set them on a path toward a successful career as a Data Scientist. Note that this role typically requires a master's degree.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers are responsible for bringing machine learning models to production. Because of this, they need to be able to train and evaluate models for real-world use. The Modeling in AWS course can help learners build the skills to do this and set them on a path toward a successful career as a Machine Learning Engineer. Note that this role typically requires a master's degree.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
Data Analysts are responsible for converting raw data into valuable insights. To do this, they need to use a variety of statistical and machine learning techniques. The Modeling in AWS course can help learners build the skills to do this and set them on a path toward a successful career as a Data Analyst. Note that this role may require an advanced degree.
See salaries and explore the career path for Data Analyst
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. Many of these systems require the use of machine learning models. The Modeling in AWS course can help learners build the skills to do this and set them on a path toward a successful career as a Software Engineer. Note that this role may require an advanced degree.
See salaries and explore the career path for Software Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. These pipelines often involve the training and evaluation of machine learning models. The Modeling in AWS course can help learners build the skills to do this and set them on a path toward a successful career as a Data Engineer. Note that this role typically requires a master's degree.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts bridge the gap between technical and non-technical stakeholders. They help to ensure that business needs are being met by technology solutions. A solid understanding of machine learning can help Business Analysts to better understand and communicate the potential benefits of machine learning to stakeholders. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Business Analyst.
See salaries and explore the career path for Business Analyst
Product Manager
Product Managers are responsible for defining the vision and roadmap for a product. They must have a deep understanding of customer needs and market trends. A solid understanding of machine learning can help Product Managers to better identify and prioritize product features that meet customer needs. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Product Manager.
See salaries and explore the career path for Product Manager
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. These models are often used to make investment decisions. A solid understanding of machine learning can help Quantitative Analysts to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Quantitative Analyst.
See salaries and explore the career path for Quantitative Analyst
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. These models are used to make decisions about a variety of topics, such as insurance premiums and pension plans. A solid understanding of machine learning can help Actuaries to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as an Actuary.
See salaries and explore the career path for Actuary
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex business problems. These models are used to make decisions about a variety of topics, such as supply chain management and logistics. A solid understanding of machine learning can help Operations Research Analysts to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as an Operations Research Analyst.
See salaries and explore the career path for Operations Research Analyst
Statistician
Statisticians use mathematical and statistical models to analyze data and draw conclusions from it. These models are used in a variety of fields, such as healthcare, finance, and education. A solid understanding of machine learning can help Statisticians to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Statistician.
See salaries and explore the career path for Statistician
Market Researcher
Market Researchers use a variety of methods to collect and analyze data about consumer behavior. This information is used to make decisions about product development and marketing campaigns. A solid understanding of machine learning can help Market Researchers to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Market Researcher.
See salaries and explore the career path for Market Researcher
Risk Manager
Risk Managers are responsible for identifying, assessing, and managing risk. They use a variety of methods to do this, including mathematical and statistical models. A solid understanding of machine learning can help Risk Managers to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Risk Manager.
See salaries and explore the career path for Risk Manager
Financial Analyst
Financial Analysts use mathematical and statistical models to analyze financial data. This information is used to make investment decisions and provide financial advice. A solid understanding of machine learning can help Financial Analysts to develop more sophisticated and accurate models. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Financial Analyst.
See salaries and explore the career path for Financial Analyst
Data Science Manager
Data Science Managers are responsible for leading and managing data science teams. They are responsible for ensuring that data science projects are aligned with business goals and that the team has the resources and support they need to be successful. A solid understanding of machine learning is essential for Data Science Managers. The Modeling in AWS course can help learners build this understanding and set them on a path toward a successful career as a Data Science Manager.
See salaries and explore the career path for Data Science Manager

Reading list

We've selected nine 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 Modeling in AWS.
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive and mathematical treatment of statistical learning methods. It covers linear regression, classification, and more.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical approach to machine learning using AWS. It covers key concepts, model training, deployment, and more.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Machine Learning in Action
Save
Provides foundational theory and a practical guide to machine learning algorithms. It covers foundational concepts like supervised and unsupervised learning, model evaluation, and more.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
Probabilistic Machine Learning
Save
Provides a probabilistic approach to machine learning. It covers Bayesian inference, graphical models, and more.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Machine Learning Con Pytorch Y Scikit Lean
Save
Provides a comprehensive and practical guide to machine learning with Python. It covers data preparation, feature engineering, and more.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Machine Learning with R
Save
Provides a practical guide to machine learning with R. It covers data preprocessing, model selection, and more.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Machine Learning for Business Analytics
Save
Provides a comprehensive guide to data mining and business intelligence. It covers data exploration, predictive modeling, and more.
Machine Learning for Business Analytics: Concepts,...
Hardcover
$$$$
Machine Learning for Business Analytics: Concepts,...
Kindle Edition
$$$$
Cover image
Machine Learning for Hackers
Save
Provides a practical and accessible introduction to machine learning for non-technical audiences.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive guide to deep learning, covering fundamental concepts, architectures, and more.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Modeling in AWS.
Launching Machine Learning: Delivering Operational...
Most relevant
GenAI for Data Science Teams
Most relevant
GenAI Chatbots: Create and Deploy OpenAI-Powered Chatbots
Most relevant
Cloud Computing Technologies - Master's
Most relevant
Getting Started with Mistral
Most relevant
Principles and Technologies of 5G Mobile Networks
Most relevant
Linear Algebra Basics
Most relevant
Source Systems, Data Ingestion, and Pipelines
Most relevant
Amazon Kendra Getting Started
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Effort
2 Weeks of Study, 2-2:30 hours/Week
Level
Introductory
Via
Coursera
Institution
Whizlabs
Instructor
Whizlabs Instructor
Language
English
This course belongs to a collection called Exam Prep MLS-C01: AWS Certified Specialty Machine Learning. It's comprised of five courses:
  1. ML Algorithms
  2. Machine Learning Implementation and Operations in AWS
  3. Exploratory Data Analysis in AWS
  4. Data Engineering in AWS
  5. Modeling in AWS (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches concepts used for modeling machine learning in the real-world
Strong match for learners who are looking to broaden their understanding of the conceptual underpinnings of machine learning models
Taught by instructors from Whizlabs, who are recognized for their work in training machine learning models
Provides hands-on labs and interactive materials, which helps to ensure that learning is practical
Focuses on the practical application of machine learning models, which could help learners develop skills that are valuable to employers
Part of a specialization, which may be appealing to learners seeking a comprehensive overview of machine learning modeling
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Modeling in AWS.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser