We may earn an affiliate commission when you visit our partners.
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.
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
Register for this course and see more details by visiting:
OpenCourser.com/course/a15lo3/modeling
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
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
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
Machine Learning Models: Performance evaluation and Tuning
Welcome to Week 2 of Modeling in AWS course. In this week, we'll deploy and evaluate performance of ML models. We'll also perform automatic model tuning in ML. By the end of this week, we'll Train a model after automatic model tuning.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save Modeling in AWS to your list so you can find it easily later:
Save
Save
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review Python Fundamentals
Show steps
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
Show steps
Show steps
- Review Python syntax and data structures
- Practice writing simple Python programs
Review AWS Cloud Services
Show steps
Familiarize yourself with key AWS services, ensuring you have a solid understanding of the cloud platform.
Browse courses on
AWS Services
Show steps
Show steps
Show steps
- Review AWS core services, such as EC2, S3, and RDS
- Explore AWS regions and availability zones
Review Machine Learning Concepts
Show steps
Refresh your understanding of foundational machine learning concepts before diving into modeling.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
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
Show steps
Review fundamental statistics and probability concepts to strengthen your foundational understanding for this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Study notes or textbooks
- Solve practice problems
- Attend a refresher workshop or tutorial
Join a Study Group or Discussion Forum
Show steps
Engage with peers and instructors in a study group or discussion forum to clarify concepts and share knowledge.
Show steps
Show steps
Show steps
- Join an online discussion forum or study group
- Participate in discussions, ask questions, and share your insights
Create a personalized study guide
Show steps
Organize and consolidate course materials to enhance your understanding and facilitate effective revision.
Browse courses on
Study Guide
Show steps
Show steps
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
Show steps
Engage in hands-on practice to solidify your understanding of training machine learning models using AWS.
Browse courses on
AWS SDK
Show steps
Show steps
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
Show steps
Supplement your understanding of modeling concepts by reviewing official AWS documentation and blog posts.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Visit the AWS Machine Learning blog
- Explore the AWS documentation for Modeling in AWS
Join a study group or discussion forum
Show steps
Engage with peers to clarify concepts, share knowledge, and provide support in your learning journey.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Supplement your learning by attending an AWS Machine Learning workshop led by industry experts.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Deepen your knowledge by applying modeling concepts to a personal project of your choice.
Show steps
Show steps
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:
Machine Learning Engineer
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.
Data Scientist
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.
Data Analyst
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.
Software Engineer
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.
Data 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.
Product Manager
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.
Quantitative Analyst
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.
Actuary
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.
Risk Manager
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.
Market Researcher
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.
Data Science Manager
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.
Business Analyst
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.
Operations Research Analyst
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.
Statistician
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.
Financial Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/a15lo3/modeling
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.
Provides a comprehensive and mathematical treatment of statistical learning methods. It covers linear regression, classification, and more.
Provides a practical approach to machine learning using AWS. It covers key concepts, model training, deployment, and more.
Provides foundational theory and a practical guide to machine learning algorithms. It covers foundational concepts like supervised and unsupervised learning, model evaluation, and more.
Provides a probabilistic approach to machine learning. It covers Bayesian inference, graphical models, and more.
Provides a comprehensive and practical guide to machine learning with Python. It covers data preparation, feature engineering, and more.
Provides a practical guide to machine learning with R. It covers data preprocessing, model selection, and more.
Provides a comprehensive guide to data mining and business intelligence. It covers data exploration, predictive modeling, and more.
Provides a practical and accessible introduction to machine learning for non-technical audiences.
Save
Provides a comprehensive guide to deep learning, covering fundamental concepts, architectures, and more.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a15lo3/modeling
Share
Similar courses
Here are nine courses similar to
Modeling in AWS.
AWS Data Processing
OpenCourser.com/course/6vafqr/aws
AWS Data Processing
Most relevant
Machine Learning Implementation and Operations in AWS
OpenCourser.com/course/9szyba/machine
Machine Learning Implementation and Operations in AWS
Most relevant
ML Algorithms
OpenCourser.com/course/2xcg6o/ml
ML Algorithms
Most relevant
Exploratory Data Analysis in AWS
OpenCourser.com/course/fwj1fy/exploratory
Exploratory Data Analysis in AWS
Most relevant
Data Engineering in AWS
OpenCourser.com/course/8niwqu/data
Data Engineering in AWS
Most relevant
AWS: CI/CD Pipelines and Deployment Strategies
OpenCourser.com/course/csfdn6/aws
AWS: CI/CD Pipelines and Deployment Strategies
Most relevant
Four Rare Machine Learning Skills All Data Scientists Need
OpenCourser.com/course/7ipo1d/four
Four Rare Machine Learning Skills All Data Scientists Need
Most relevant
MLOps Platforms: Amazon SageMaker and Azure ML
OpenCourser.com/course/jq0kjn/mlops
MLOps Platforms: Amazon SageMaker and Azure ML
Most relevant
AWS: Security in Data Analytics
OpenCourser.com/course/le2c0k/aws
AWS: Security in Data Analytics
Most relevant
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:
Good to know
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
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 - 2024 OpenCourser