Presented by Aoife D’ArcyAoife has spent the last 15 years developing analytical models and processes for major national and international companies in banking, finance, insurance, gaming and manufacturing. Aoife has developed particular expertise in customer insight analytics, fraud analytics, and risk analytics.Aoife founded The Analytics Store in 2009 to peruse her passionate belief in the importance of developing in-house analytics talent in organisations. Aoife works with organisations to help them build world class analytics teams and processes through a unique mix of training, consultancy and mentoring.Aoife is a co-author of the textbook "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples & Case Studies" published in 2015 with MIT Press.
Introduction Machine learning and predictive data analytics are fast becoming the best way for sophisticated organisations to use data to gain a competitive edge. Predictive analytics applications use machine learning to build predictive models for applications including price prediction, risk assessment, and predicting customer behaviour. Based on the trainers’ book, “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” (www.machinelearningbook.com) this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.All delegates receive a free copy of the book “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies”
Learn how to
This course has been designed to guide delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models using SAS. After completing the course delegates will:
- Understand how to frame a business problem as a predictive analytics problem
- Understand the fundamental theories of machine learning, and the most important machine learning approaches
- Be familiar with a wide range of applications of predictive data analytics and machine learning, including the limitations of machine learning
- Be comfortable analysing the quality of datasets for machine learning models
- Have an awareness of how SAS can be used to build predictive analytics models using machine learning techniques
- Be fully prepared to understand newly emerging advanced topics in machine learning
Who should attend This course is aimed at people in a technical role who want to fully understand and use machine learning based predictive analytics techniques. This course is for you if:you need to learn about the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics modelsyou need to learn how to apply detailed examples and real-world case studies using SAS technology.you like learning from experts, in an instructor led class room environmentyou are interested in understanding sophisticated machine learning theories, and how they are applied to enable best business practice
To attend this course delegates should be familiar with basic statistical concepts (such as mean, standard deviation, and correlation) and comfortable with data manipulation tools such as spreadsheets and databases. Some knowledge of SAS would be useful but not essential.