Core Concepts in AI
Master AI fundamentals, from machine learning algorithms to data quality and resource management, bridging technical concepts with strategic execution.
Master AI fundamentals, from machine learning algorithms to data quality and resource management, bridging technical concepts with strategic execution.
This comprehensive course builds a strong foundation in artificial intelligence and machine learning concepts, focusing on practical implementation and strategic decision-making. You'll learn the essential R.O.A.D. Framework (Requirements, Operationalize Data, Analytic Method, Deployment) for effective AI project management and explore key performance metrics to evaluate machine learning models. The course delves into algorithm selection, analyzing strengths and weaknesses of various approaches including Support Vector Machines, Decision Trees, and Neural Networks. You'll develop critical skills in assessing data quality, calculating inter-annotator agreement, and navigating the tradeoffs between computational resources and performance. Designed for both technical and non-technical professionals, this course bridges theoretical concepts with practical applications, empowering you to make informed decisions about AI implementation and align AI initiatives with organizational goals.
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What you'll learn
Understand key AI terminology and concepts to communicate effectively in AI projects
Apply the R.O.A.D. Framework to structure and manage AI implementations systematically
Evaluate machine learning models using appropriate performance metrics
Select optimal algorithms based on problem requirements and resource constraints
Assess data quality and calculate inter-annotator agreement for labeled datasets
Identify strengths and weaknesses of different machine learning approaches
Skills you'll gain
This course includes:
12 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This course provides a comprehensive exploration of artificial intelligence and machine learning fundamentals, carefully balancing technical depth with strategic application. The curriculum begins with an introduction to AI concepts and the practical R.O.A.D. Framework for project management. Students then delve into the statistical foundations of machine learning, covering hypothesis testing and performance metrics. The course examines algorithm tradeoffs across various methods including Support Vector Machines, Naïve Bayes, Decision Trees, Random Forest, and Neural Networks, highlighting their strengths and weaknesses for different problem types. Data quality considerations are thoroughly addressed, with special attention to labeling challenges, cognitive limitations, and inter-annotator agreement measurement. The final module covers essential resource management in AI systems, including memory optimization, computational tradeoffs, and performance considerations. Throughout the course, theoretical concepts are reinforced through practical scenarios and real-world applications, making it ideal for both technical practitioners and decision-makers responsible for AI implementation.
Course Introduction
Module 1 · 9 Minutes to complete
Introduction to Artificial Intelligence
Module 2 · 6 Hours to complete
Machine Learning
Module 3 · 2 Hours to complete
Algorithm Tradeoffs
Module 4 · 3 Hours to complete
Data
Module 5 · 4 Hours to complete
Resources
Module 6 · 6 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Pioneering Social Network Analysis and AI at Johns Hopkins University
Dr. Ian McCulloh is an esteemed associate professor at Johns Hopkins University, holding joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. His research focuses on social neuroscience, social network analysis, and the application of artificial intelligence to enhance understanding of online influence and strategic communication. With over 100 peer-reviewed publications and several influential books, including Social Network Analysis with Applications and ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror, Dr. McCulloh has established himself as a leading voice in his field. He also founded the Brain Rise Foundation, a nonprofit dedicated to advancing neuroscience research for substance abuse recovery. Prior to his academic career, he had a distinguished military service, retiring as a Lieutenant Colonel after 20 years, during which he led innovative projects in data-driven social science research for countering extremism. Dr. McCulloh's multifaceted expertise and commitment to applying science for societal benefit make him a valuable asset to both academia and public health initiatives.
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