Machine learning (ML) has become a cornerstone capability for today’s data-centric organization. At the TDWI Machine Learning Bootcamp, you will master the strategies, techniques, and best practices that produce understandable and actionable insights for maximum business impact.
Each day of this seminar focuses on a distinct category of machine learning solutions, showing you how to identify business goals, prepare data, select machine learning algorithms, and deploy models for multiple classes of analytics problems. Most important, the instructor will explore how to explain the findings from each model type and transform them into measurable actions.
The Machine Learning Bootcamp begins with supervised learning techniques for estimation, classification, and prediction. You will learn when to use these foundational techniques, understand best practices in supervised machine learning, and take a deep dive into regression models and decision trees. You will also learn deployment practices for model interpretation and automation.
The second day probes deeper into advanced techniques for deeper insights, exposing you to ensemble modeling techniques that support bagging, boosting, and stacking. You will also learn how explainable AI (XAI) techniques are applied to help make sense of what might otherwise be considered “black box” models. XAI solutions help ensure business adoption through global and local explanation techniques.
The final day of the Machine Learning Bootcamp digs into unsupervised machine learning techniques that support clustering and association. You will learn how to choose between supervised and unsupervised methods for any given business problem and how to select algorithms, methods, and principles for unsupervised machine learning. You will also learn interpretation techniques, deployment approaches, and how a mixture of models including supervised, ensemble, and unsupervised solutions are used together in real-world situations to solve business challenges.
Attend all three days and earn a certificate.