Successful companies realised the power of data driven decision making a few decades back when analytics became a lever to succeed. Over the years the landscape evolved and became much more complex, with large volumes of complex data streaming in and being stored, waiting to be analysed. In order to analyse this new era data, companies need newer technologies and algorithms in order to extract the insights needed to make business impact. This is where Machine Learning comes in handy.
Machine Leaning helps to solve business challenges with the help of data. Today's business challenges start with large volumes of complex data. Effective decision-making requires state-of-the-art techniques for predictive modeling. In this course, you learn about the three main requirements for moving rapidly from data to decisions: 1) state-of-the-art techniques for predictive modeling: machine learning; 2) powerful and easy-to-use software that can help you wrangle your data into shape and quickly create many accurate predictive models: SAS Viya and related tools; 3) and an integrated process to manage your analytical models for optimal performance throughout their lifespan.
This course aims to provide students with a broad coverage and examples of enterprise analytics techniques with special focus on supervised machine learning techniques and applications. Upon completion of the course, students will be able to:
- Work with data from exploration to pattern discover to deployment
- Prepare the data and create new powerful features
- Build powerful machine learning models efficiently
- Assess each supervised model using the appropriate criterion
- Apply robust supervised algorithms such as decision trees, gradient boosting models, forests, neural networks and support vector machines.
- Develop expertise in using SAS machine learning tool called Model Studio in SAS Viya
- Build Machine Learning pipelines in SAS Model Studio
- Deploy and manage machine learning models in production
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