The course "Core Concepts in AI" provides a comprehensive foundation in artificial intelligence (AI) and machine learning (ML), equipping learners with the essential tools to understand, evaluate, and implement AI systems effectively. From decoding key terminology and frameworks like R.O.A.D. (Requirements, Operationalize Data, Analytic Method, Deployment) to exploring algorithm tradeoffs and data quality, this course offers practical insights that bridge technical concepts with strategic decision-making.
Core Concepts in AI
Core Concepts in AI
This course is part of AI Strategy and Project Management Specialization
Instructor: Ian McCulloh
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand core AI and ML concepts, key vocabulary, and the R.O.A.D. Framework for effective AI project management and implementation.
Evaluate machine learning models using performance metrics and understand the tradeoffs in algorithm selection and optimization.
Analyze AI algorithms like SVM, Decision Trees, and Neural Networks, identifying their strengths, weaknesses, and practical applications.
Assess data quality, calculate inter-annotator agreement, and address resource and performance tradeoffs in AI and ML systems.
Skills you'll gain
Details to know
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Recently updated!
December 2024
Assessments
15 assignments
Taught in English
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This course is part of the AI Strategy and Project Management Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
This course provides a comprehensive introduction to key concepts in artificial intelligence (AI) and machine learning (ML). Learners will explore essential vocabulary, the R.O.A.D. Framework, performance evaluation, and algorithm tradeoffs. Topics include data quality, inter-annotator agreement, and the strengths and weaknesses of AI methods. By the end, learners will be equipped with the foundational knowledge to navigate and assess AI and ML systems effectively.
What's included
1 reading1 plugin
This module provides an introduction to artificial intelligence (AI). It does not require any prior knowledge of AI and is suitable for briefing managerial, and non-technical leaders to improve knowledge, expectations, and communication for AI projects.
What's included
6 videos4 readings3 assignments
This module covers the statistical foundations of machine learning and the common metrics for evaluating machine learning and artificial intelligence performance.
What's included
6 videos2 readings3 assignments
This module introduces the most common algorithms used in AI and machine learning, including support vector machines, Naïve Bayes, decision trees, random forest, and neural networks. We will discuss the strengths and weaknesses of these algorithms for different classes of problems.
What's included
8 videos2 readings3 assignments
This module explores data types (nominal, ordinal, categorical) and the challenges of data labeling, including human cognitive limits and reference issues. A key focus is inter-annotator agreement—a method to measure labeling consistency, highlighting biases and inefficiencies in human and machine processes. Consistent labeling, often more impactful than advanced algorithms, is crucial for responsible AI.
What's included
9 videos2 readings3 assignments
This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.
What's included
10 videos2 readings3 assignments
Instructor
Offered by
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Johns Hopkins University
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