Artificial Intelligence for Business

This module will begin with a definition of Big Data, an exploration of its origins, and why the ways it is produced matter. You’ll examine the most useful approaches to Big Data analysis, and learn which skillset and competencies are are required for Big Data analysis. You’ll also explore different data management and data analysis tools and discover how predictive analysis is used to extract intelligence from Big Data. By the end of this module, you’ll be better able to analyze big datasets, choose the right tools for analysis, and harness insights generated from Big Data to construct successful strategies for your business.

Module Overview:

• AI for Business Introduction
• Big Data Overview
• Big Data Analysis
• Data Management Infrastructure
• Data Analysis: Extracting Intelligence from Big Data

In this module, you’ll examine the fundamentals of Artificial Intelligence, and delve deeper into Machine Learning. Through close examination of the history of AI and the expert systems approach, you’ll gain a deeper understanding of AI’s definition and types. You’ll also learn three types of Machine Learning (supervised, unsupervised, and reinforcement learning) and examine the differences between Machine Learning and AI. You’ll also explore factors that influence accuracy in Machine Learning, as well as analyze specific Machine Learning methods such as logistic regression, decision trees, and neural networks. By the end of this module, you will have a better understanding of both Artificial Intelligence and Machine Learning and be able to select appropriate algorithms and methods to optimize your business’ trajectory.

Module Overview:

• Introduction to Artificial Intelligence
• A Detailed View of Machine Learning
• Specific Machine Learning Methods: A Deep Dive

In this module, you will explore real-world examples of Machine Learning in different business contexts, including personalization on the web, financial applications, and autonomous vehicles. You’ll learn about multiple applications of Machine Learning in finance, such as fraud detection and identity verification, as well as the opportunities and challenges of autonomous vehicles. Through analysis of various Recommender Systems, you’ll better understand their impact on markets and be able to address the challenges of each. By the end of this module, you’ll have a richer understanding of existing Machine Learning technologies and how they are transforming industries and markets.

Module Overview:

• Business Applications of Machine Learning and Personalization
• Personalization: Impacts on Markets
• Personalization: Addressing the Challenges
• Interview with Apoorv Saxena
• Machine Learning in Finance: Fraud Detection
• Machine Learning in Finance: Additional Applications
• Autonomous Vehicles (AVs)
• Challenges to Adoption

In this module, you’ll explore how to strategically implement AI within your organization and manage AI governance. You’ll examine how to develop a portfolio approach of AI projects and learn how quick wins and long-term projects can help companies successfully utilize the power of machine intelligence. You’ll also analyze specific organizational behaviors that help organizations generate value from AI. Through a series of examples such as Xaoice and Tay, you’ll learn about the risks from AI and the social risks AI presents for firms. By the end of this module, you’ll be able to better navigate the risks of AI and construct a more efficient and successful AI strategy for your business.

Module Overview:

• Interview with Apoorv Savena
• AI-Driven Business Transformation
• Developing a Portfolio for AI Projects
• Lowering Barriers for AI Use
• AI in the Organizational Structure
• Risks with AI
• Governance
• Course Takeaways