Machine Learning Using SAS Viya

Go to Course: https://www.coursera.org/learn/machine-learning-sas

Introduction

### Course Review: Machine Learning Using SAS Viya on Coursera If you’re venturing into the world of machine learning and are eager to acquire robust practical skills while grounding yourself in theoretical knowledge, the course **"Machine Learning Using SAS Viya"** on Coursera is an excellent choice. This unique course not only familiarizes you with SAS Viya, a powerful analytics platform, but also guides you through a business-centric analytical life cycle. Here’s a detailed breakdown of what you can expect from the course. #### Course Overview This course is meticulously designed to cover the essential techniques associated with supervised machine learning models. You’ll start by immersing yourself in the analytical life cycle, from understanding business problems to deploying coherent models. By the end of the course, you should feel confident in preparing data, selecting features, training and validating models, and assessing their performance. ### Syllabus Breakdown 1. **Course Overview** - The course begins with an introduction to the instructor and logistics. It’s efficient and sets a clear path for what’s to come. 2. **Getting Started with Machine Learning Using SAS® Viya** - This module equips you with the foundational knowledge of how machine learning can tackle contemporary business challenges. You’ll also embark on a project that will run throughout the course, providing a hands-on learning experience. 3. **Data Preparation and Algorithm Selection** - You will learn the intricacies of data exploration and preparation, essential for any machine learning project. This module emphasizes understanding your data and selecting appropriate algorithms based on general considerations. 4. **Decision Trees and Ensembles of Trees** - Here, you’ll dive into building decision tree models as well as advanced ensemble models. This is a crucial segment of supervised learning and will prepare you to model complex datasets. 5. **Neural Networks** - This module accesses one of the most celebrated areas in machine learning: deep learning through neural networks. You will learn to build and implement these models effectively. 6. **Support Vector Machines** - You will learn how Support Vector Machines (SVM) work, along with practical exercises for constructing these models. SVM is a powerful classification technique that can handle high-dimensional data. 7. **Model Deployment** - Finally, the course culminates in teaching how to select the best-performing model for your specific business challenge and introduce it into production. This does not only involve deployment but also encompasses ongoing model management. 8. **Additional Resources and Practice Exam** - To reinforce your learning, this module provides supplementary resources and a practice exam, ensuring you grasp the course concepts thoroughly. ### Review and Recommendations **Strengths:** - **Hands-On Project:** The continuous project throughout the course is an excellent feature. It allows for practical application of learned theories, making the learning process more engaging and relevant. - **Wide Range of Techniques:** Training covers essential machine learning techniques, ensuring you develop a well-rounded understanding of model building. - **Business-Focused:** Emphasizing real-world application, the course is designed to address business challenges, providing you with skills that are directly relatable to the corporate environment. **Considerations:** - **Software Dependency:** The course requires using SAS Viya, which may involve a learning curve if you are not familiar with it. However, SAS provides a rich environment for data analytics, which can be a worthwhile investment. - **Pacing:** The modules are designed to build upon each other, so it’s recommended to pursue them in the prescribed order for optimal understanding. ### Conclusion Overall, **"Machine Learning Using SAS Viya"** on Coursera is a commendable course for anyone looking to deepen their knowledge in machine learning while using a practical approach. The combination of theory, practice, and business insights prepares participants not just to understand machine learning but also to apply it effectively within a business context. Whether you’re a novice or have some experience in the field, this course is suitable for advancing your skills and knowledge. I wholeheartedly recommend this course to anyone seeking to enhance their capabilities in machine learning and data analytics. It fills the gap between theoretical concepts and practical application, crucial for success in today’s data-driven world.

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.

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.

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.

In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.

In this module, you learn to build neural network models.

In this module, you learn to build support vector machine models.

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.