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TDWI Machine Learning Bootcamp

A TDWI Certificate Track

Virtual Classroom
July 10–12, 2023
9:00am – 5:00pm CT

Keith McCormick

Pandata, LLC

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.

  • Monday, July 10

    Supervised Machine Learning: Building and Deploying Analytic Models for Classification and Prediction

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    Supervised learning techniques such as regression, decision trees, and neural networks provide powerful predictive insights. Applied properly, these data-driven insights expose the forces that shape your organization’s outcomes. Models built using these techniques can produce key indicators that optimize the allocation of organizational resources. This course provides the best practices that guide supervised machine learning processes, covering decision trees and regression models in detail.

  • Tuesday, July 11

    Predictive Modeling with Ensembles and XAI: Advanced Techniques for Deeper Insights

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    This course both prepares you to put ensemble techniques to use in your business and covers the XAI techniques that ensure understandable results. This course will discuss two kinds of explanations: global and local. Global explanations describe overall patterns in the model, notably which variables are most and least important. Local explanations describe why a particular case received a prediction. For example, why was a specific loan predicted to default? Regulated industries are often especially interested in XAI, but anyone that is considering complex machine learning models can benefit from a knowledge of XAI techniques.

  • Wednesday, July 12

    Unsupervised Machine Learning: Preparing Data and Deploying Analytic Models for Clustering and Association

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    In this full-day course, the instructor will demonstrate a variety of examples starting with the correct data structure for these methods. Alignment between data format and problem definition will be reviewed. Different emphases of the business problem will often imply modifications to the way that the data is prepared. The course will then proceed with the exploration and interpretation of candidate models. Options for acting on results will be explored. You will also observe how a mixture of models including business rules, supervised models, and unsupervised models are used together in real-world situations for various problems like insurance and fraud detection.

Pricing Select Courses, View Pricing

3-Day Certificate Unlocked

Discounts

  • Early Bird Discounts

    Receive a 20% discount if you register by May. 12.
    Receive a 10% discount if you register by Jun. 9.

  • Member Discount

    Receive a 10% discount on your registration. Membership status will be validated when your registration is processed.

  • Team Discounts

    Teams of 3–9 people save 10% by using code TEAM. Groups of 10 or more will save 20% with code TEAM20.

REFUND AND CANCELLATION: You may substitute one person in your place by contacting registration@tdwi.org at least five business days prior to the event. If you must cancel all or part of your registration, your refund request must be sent to registration@tdwi.org no later than June 23, 2023. Your fee will be returned, less a 20% cancellation fee. No refunds will be issued after June 23, 2023.

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