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Jeff Thompson
and
Catherine Truxillo
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.
Read more
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.
Read more
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.
This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.
Register for this course and see more details by visiting:
OpenCourser.com/course/8qd9zd/machine
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What's inside
Syllabus
Course Overview
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
Getting Started with Machine Learning using SAS® Viya
Read more
Syllabus
Course Overview
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
Getting Started with Machine Learning using SAS® Viya
Read more
In this module, you learn how you can meet today's business challenges with machine learning using SAS® Viya®. You start working on the project that runs throughout the course.
Data Preparation and Algorithm Selection
In this module, you learn to explore the data and finish preparing the data for analysis. You also learn some general considerations for selecting an algorithm.
Decision Trees and Ensembles of Trees
In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.
Neural Networks
In this module, you learn to build neural network models.
Support Vector Machines
In this module, you learn to build support vector machine models.
Model Deployment
In this module, you learn how to select the model that best meets the requirements of your business challenge and put the model into production. You also learn about managing the model over time.
Additional Resources and Practice Exam
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This course is ideal for anyone interested in enhancing their decision-making capabilities in the business world
Provides a structured approach to problem-solving that can be applied to real-world business challenges
Leverages SAS Viya, a powerful analytics platform widely used in industry
Combines theoretical understanding with hands-on practice through demonstrations and exercises
Covers a range of supervised machine learning techniques, enabling learners to choose the most appropriate model for their specific business needs
Guides learners through the entire analytical life cycle, from problem framing to model deployment and management
Save this course
Save Machine Learning Using SAS Viya to your list so you can find it easily later:
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Reviews summary
Machine learning with sas viya
Learners say this SAS Viya machine learning course is well received with good explanations for beginners and engaging assignments. The course aims to provide a good foundation in core AI concepts, especially for students with limited prior machine learning experience. Learners may wish for more emphasis on model deployment and testing during the course.
Course may prepare you for a certification exam.
"It enriches the knowledge of anyone working with predictive modeling and even prepares you for certification."
This interactive course includes good assignments.
"A well designed and thoughtful course explaining the key concepts of machine learning"
Well received by learners without prior knowledge on ML.
"Easy to follow even with limited statistics knowledge."
"The instructors teach the basics to get you started and best of all the software is included so you can practice."
Course focuses on SAS Viya for building machine learning models.
"The instructors of this course did a great job in explaining SAS Viya tools to build machine pipelines."
Course lacks emphasis on model deployment and testing.
"I liked the course. However too little time was spend on model deployment."
"There was none on testing. This is the most important part of developing a model."
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 Machine Learning Using SAS Viya with these
activities:
Review Linear Algebra Fundamentals
Show steps
Refresh your understanding of linear algebra fundamentals to strengthen your foundation for supervised machine learning models.
Browse courses on
Linear Algebra
Show steps
-
Revisit concepts such as vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
-
Explore applications of linear algebra in machine learning, such as dimensionality reduction.
Review Python Machine Learning Libraries
Show steps
Solidify your understanding of Python machine learning libraries to enhance your comprehension of supervised machine learning models.
Browse courses on
Python
Show steps
-
Revisit basic Python syntax and data structures.
-
Explore fundamental machine learning libraries like scikit-learn, TensorFlow, and Keras.
-
Practice implementing algorithms such as linear regression and decision trees using these libraries.
Decision Tree Analysis Practice
Show steps
Enhance your grasp of decision tree models by completing targeted practice exercises.
Show steps
-
Work through guided examples of decision tree algorithms.
-
Solve practice problems involving the construction and interpretation of decision trees.
-
Analyze real-world datasets using decision tree models.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Supervised Learning Model Discussion Group
Show steps
Engage in discussions with peers to clarify concepts and share insights on supervised learning models.
Show steps
-
Join a study group or online forum dedicated to supervised learning.
-
Participate in discussions on model selection, feature engineering, and evaluation techniques.
-
Present your own analysis and findings to receive feedback and broaden your perspective.
Neural Network Implementation Tutorial
Show steps
Advance your understanding of neural network models through hands-on implementation.
Show steps
-
Follow a step-by-step tutorial on neural network architecture and training.
-
Implement neural networks in a programming environment.
-
Experiment with different neural network configurations and hyperparameters.
Supervised Learning Model Presentation
Show steps
Develop a presentation on a supervised learning model to enhance your communication skills and deepen your understanding.
Show steps
-
Choose a supervised learning model and gather relevant data.
-
Train and evaluate the model using appropriate metrics.
-
Prepare a clear and concise presentation that explains the model, its performance, and its potential applications.
-
Deliver your presentation to peers or industry professionals to receive feedback and expand your network.
Data Preparation and Algorithm Selection Guide
Show steps
Create a comprehensive guide to data preparation and algorithm selection to solidify your understanding and assist others.
Show steps
-
Gather resources and best practices on data preparation techniques.
-
Summarize key considerations for selecting appropriate machine learning algorithms.
-
Develop a step-by-step guide that outlines the process of data preparation and algorithm selection.
-
Share your guide with peers or publish it online to contribute to the community.
Data Science Project Involvement
Show steps
Gain practical experience by participating in data science projects, solidifying your skills and expanding your portfolio.
Show steps
-
Identify organizations or projects seeking volunteers with data science expertise.
-
Contribute to data analysis, model building, or data visualization tasks.
-
Collaborate with professionals to apply supervised learning techniques to real-world problems.
Contribute to Open-Source Machine Learning Projects
Show steps
Engage with the open-source community and enhance your understanding by contributing to machine learning projects.
Show steps
-
Identify open-source machine learning projects aligned with your interests.
-
Explore the codebase and documentation to familiarize yourself with the project.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit your contributions and engage with the community to refine your work.
Review Linear Algebra Fundamentals
Show steps
Refresh your understanding of linear algebra fundamentals to strengthen your foundation for supervised machine learning models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit concepts such as vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Explore applications of linear algebra in machine learning, such as dimensionality reduction.
Review Python Machine Learning Libraries
Show steps
Solidify your understanding of Python machine learning libraries to enhance your comprehension of supervised machine learning models.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit basic Python syntax and data structures.
- Explore fundamental machine learning libraries like scikit-learn, TensorFlow, and Keras.
- Practice implementing algorithms such as linear regression and decision trees using these libraries.
Decision Tree Analysis Practice
Show steps
Enhance your grasp of decision tree models by completing targeted practice exercises.
Show steps
Show steps
Show steps
- Work through guided examples of decision tree algorithms.
- Solve practice problems involving the construction and interpretation of decision trees.
- Analyze real-world datasets using decision tree models.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Supervised Learning Model Discussion Group
Show steps
Engage in discussions with peers to clarify concepts and share insights on supervised learning models.
Show steps
Show steps
Show steps
- Join a study group or online forum dedicated to supervised learning.
- Participate in discussions on model selection, feature engineering, and evaluation techniques.
- Present your own analysis and findings to receive feedback and broaden your perspective.
Neural Network Implementation Tutorial
Show steps
Advance your understanding of neural network models through hands-on implementation.
Show steps
Show steps
Show steps
- Follow a step-by-step tutorial on neural network architecture and training.
- Implement neural networks in a programming environment.
- Experiment with different neural network configurations and hyperparameters.
Supervised Learning Model Presentation
Show steps
Develop a presentation on a supervised learning model to enhance your communication skills and deepen your understanding.
Show steps
Show steps
Show steps
- Choose a supervised learning model and gather relevant data.
- Train and evaluate the model using appropriate metrics.
- Prepare a clear and concise presentation that explains the model, its performance, and its potential applications.
- Deliver your presentation to peers or industry professionals to receive feedback and expand your network.
Data Preparation and Algorithm Selection Guide
Show steps
Create a comprehensive guide to data preparation and algorithm selection to solidify your understanding and assist others.
Show steps
Show steps
Show steps
- Gather resources and best practices on data preparation techniques.
- Summarize key considerations for selecting appropriate machine learning algorithms.
- Develop a step-by-step guide that outlines the process of data preparation and algorithm selection.
- Share your guide with peers or publish it online to contribute to the community.
Data Science Project Involvement
Show steps
Gain practical experience by participating in data science projects, solidifying your skills and expanding your portfolio.
Show steps
Show steps
Show steps
- Identify organizations or projects seeking volunteers with data science expertise.
- Contribute to data analysis, model building, or data visualization tasks.
- Collaborate with professionals to apply supervised learning techniques to real-world problems.
Contribute to Open-Source Machine Learning Projects
Show steps
Engage with the open-source community and enhance your understanding by contributing to machine learning projects.
Show steps
Show steps
Show steps
- Identify open-source machine learning projects aligned with your interests.
- Explore the codebase and documentation to familiarize yourself with the project.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit your contributions and engage with the community to refine your work.
Career center
Learners who complete Machine Learning Using SAS Viya will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of machine learning to find insights from data. This course is a great first step towards becoming a Data Scientist as it teaches key concepts such as data preparation, feature selection, and model training and validation. The course also provides hands-on experience with SAS Viya, a leading data science platform, and covers topics such as decision trees, neural networks, and support vector machines. These topics are essential for developing machine learning models that can solve real-world business problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. This course provides a solid foundation in the theoretical principles behind supervised machine learning models, as well as practical experience using SAS Viya to train and deploy models. The course covers a variety of machine learning algorithms, including decision trees, neural networks, and support vector machines, providing learners with the skills they need to be successful in this role.
Data Analyst
Data Analyst
Data Analysts use machine learning to analyze data and derive insights. This course provides a strong foundation in the concepts and techniques used in machine learning, with a focus on supervised learning. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Data Analysts develop the skills they need to use machine learning to solve business problems.
Business Analyst
Business Analyst
Business Analysts use data to make informed decisions about business strategies and operations. This course provides Business Analysts with a foundational understanding of machine learning and its applications in business. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Business Analysts develop the skills they need to use machine learning to improve business outcomes.
Statistician
Statistician
Statisticians use machine learning to analyze data and derive insights. This course provides Statisticians with a foundation in the theoretical principles behind supervised machine learning models, as well as practical experience using SAS Viya to train and deploy models. The course covers a variety of machine learning algorithms, including decision trees, neural networks, and support vector machines, providing learners with the skills they need to be successful in this role.
Consultant
Consultant
Consultants use their knowledge of machine learning to help organizations solve business problems. This course provides Consultants with a comprehensive understanding of the machine learning lifecycle, from problem understanding to model deployment. The course covers a variety of machine learning algorithms, including decision trees, neural networks, and support vector machines, as well as hands-on experience with SAS Viya. This course can help Consultants develop the skills they need to be successful in this role.
Software Engineer
Software Engineer
Software Engineers use machine learning to develop new products and applications. This course provides Software Engineers with a foundational understanding of the principles and practices of machine learning. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Software Engineers develop the skills they need to use machine learning to build innovative software.
Product Manager
Product Manager
Product Managers use machine learning to improve the development and marketing of products. This course provides Product Managers with a foundational understanding of machine learning and its applications in product management. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Product Managers develop the skills they need to use machine learning to build better products.
Marketer
Marketer
Marketers use machine learning to target and personalize marketing campaigns. This course provides Marketers with a foundational understanding of machine learning and its applications in marketing. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Marketers develop the skills they need to use machine learning to improve their marketing campaigns.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to analyze financial data and make investment decisions. This course provides Financial Analysts with a foundational understanding of machine learning and its applications in finance. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Financial Analysts develop the skills they need to use machine learning to make better investment decisions.
Sales Manager
Sales Manager
Sales Managers use machine learning to improve sales forecasting and customer targeting. This course provides Sales Managers with a foundational understanding of machine learning and its applications in sales. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Sales Managers develop the skills they need to use machine learning to improve their sales performance.
Operations Manager
Operations Manager
Operations Managers use machine learning to improve operational efficiency and productivity. This course provides Operations Managers with a foundational understanding of machine learning and its applications in operations. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Operations Managers develop the skills they need to use machine learning to improve their operations.
Healthcare Manager
Healthcare Manager
Healthcare Managers use machine learning to improve the delivery and efficiency of healthcare services. This course provides Healthcare Managers with a foundational understanding of machine learning and its applications in healthcare. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Healthcare Managers develop the skills they need to use machine learning to improve the quality and efficiency of healthcare delivery.
Recruiter
Recruiter
Recruiters use machine learning to find and attract top talent. This course provides Recruiters with a foundational understanding of machine learning and its applications in recruiting. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help Recruiters develop the skills they need to use machine learning to improve their recruiting efforts.
IT Manager
IT Manager
IT Managers use machine learning to improve the efficiency and security of their IT systems. This course provides IT Managers with a foundational understanding of machine learning and its applications in IT. The course covers topics such as data preparation, feature selection, and model training and validation, as well as hands-on experience with SAS Viya. This course can help IT Managers develop the skills they need to use machine learning to improve the performance and security of their IT systems.
For more career information including salaries, visit:
OpenCourser.com/course/8qd9zd/machine
Reading list
We've selected 13 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
Machine Learning Using SAS Viya.
Classic textbook on statistical learning that provides a good foundation for machine learning.
Provides a comprehensive overview of machine learning concepts and techniques from a mathematical perspective. It good choice for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
More advanced textbook on statistical learning that provides a comprehensive overview of the field.
Provides a comprehensive overview of machine learning using PyTorch.
Helpful resource for learning about machine learning using SAS Enterprise Miner.
Provides a probabilistic perspective on machine learning, which is helpful for understanding the foundations of the field.
Provides a comprehensive overview of machine learning using Python.
Provides a comprehensive overview of machine learning using R.
Provides an introduction to Bayesian reasoning and its applications to machine learning.
Is an introduction to reinforcement learning, a subfield of machine learning that deals with learning how to make decisions in a sequential environment.
Provides a gentle introduction to machine learning concepts and techniques. It good starting point for learners who are new to machine learning.
Save
Provides a comprehensive overview of deep learning concepts and techniques. It good choice for learners who want to gain a deeper understanding of the theoretical foundations of deep learning.
Provides a comprehensive overview of machine learning concepts and techniques in a clear and accessible way. It good choice for learners who want to get a broad understanding of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8qd9zd/machine
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Level
Intermediate
Via
Coursera
Institution
SAS
Instructors
Jeff Thompson
Catherine Truxillo
Language
English
Transcripts
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Deutsch
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中文
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Good to know
This course is ideal for anyone interested in enhancing their decision-making capabilities in the business world
Provides a structured approach to problem-solving that can be applied to real-world business challenges
Leverages SAS Viya, a powerful analytics platform widely used in industry
Combines theoretical understanding with hands-on practice through demonstrations and exercises
Covers a range of supervised machine learning techniques, enabling learners to choose the most appropriate model for their specific business needs
Guides learners through the entire analytical life cycle, from problem framing to model deployment and management
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