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Udacity

AI for Business Leaders

BMW via Udacity Nanodegree

Overview

AI is utilized by companies like Google, IBM, Apple, Amazon, and more to create sustainable competitive advantage in terms of productivity, technological advancement, and enhanced customer experiences. Despite Artificial Intelligence becoming increasingly accessible and ubiquitous, 94% of enterprises are struggling to implement AI in their organizations. The AI for Business Leaders Executive Program will empower you to strategically implement AI in your organization so you can leverage such revolutionary technologies for corporate growth. You’ll develop foundational technical knowledge of machine learning and the business applications of artificial intelligence across industries. Through practical case studies, you’ll learn what strategic questions to ask, and how to formulate proposals when evaluating opportunities to embed machine learning processes and artificial intelligence technology into a corporate strategy. Finally, in the capstone project, you will build an AI-backed strategy that can be integrated into your own business!
Master the foundations of artificial intelligence so you can strategically implement machine learning technology in your organization, and leverage it for corporate growth.

Syllabus

Prerequisite Knowledge

To optimize your chances of success in this Executive Program, we recommend prior exposure to statistics and probability, as well as experience in business decision-making in an IT or technical environment.See detailed requirements.

  • The Paradigm Shift

    Understand how to apply probabilistic reasoning to machine learning, and gain a working knowledge of the key terms and components involved in machine learning approaches, such as: algorithm, model, training, feature, test set, training set, and ground truth dataset. Then, develop ideas for machine learning and AI use cases for a business, and evaluate them for feasibility and impact.

  • The Math Behind the Magic

    Understand how critical data attributes can affect a machine learning model, and distinguish the differences between classification, regression, optimization, and simulation in ML/AI applications. Become familiar with the applications of deep learning and how it can be applied to predictive modeling, reinforcement learning models, and optimization.

  • Architectures of AI Systems

    Understand the importance, applications, and components of machine learning model architecture including classifiers, regressors, optimizers, simulators, policy learners, and segmenters. Differentiate between the capabilities of natural language processing, voice/speech processing, and computer vision. Finally, build machine learning model architectures for a digital channel chatbot, negotiation engine, and visual classifier.

  • Working with Data

    Learn how to label data for supervised learning. Understand the fundamental requirements of AI infrastructure, and how to overcome common implementation hurdles. Assess the feasibility of AI use cases in a range of business scenarios by evaluating data readiness.

  • Accuracy, Bias, and Ethics

    Define the parameters for designing machine learning models including accuracy, underfitting and overfitting of data, and ethical frameworks.

  • Gathering Feedback

    Learn how to build surveys and conduct interviews to solicit feedback on model prototypes. Identify key stakeholders inside and outside an organization to provide feedback in an iterative design process. Analyze the results of feedback from stakeholders to inform evaluation and prioritization of business use cases.

  • Thinking Bigger

    Learn how to begin implementing AI use cases with small learning experiments, and build a roadmap deploying machine learning applications that strategically complement one another. Finally, create a proposal that integrates key use cases into a transformational business story.

  • CAPSTONE PROJECT - Deliver a Machine Learning / AI Strategy

    Draw on all of the skills learned throughout the lessons to create an ML/AI strategy that is technically achievable and highly impactful on your business based on the evaluation of various AI-enabled use cases.

    Project Details  

Taught by

William Ross, Luis Serrano and Josh Bernhard

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