Core Concepts in AI

Core Concepts in AI

This course is part of AI Strategy and Project Management Specialization

Instructor: Ian McCulloh

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.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 6 modules in this course

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.

What sets this course apart is its focus on balancing technical depth with accessibility, making it ideal for leaders, managers, and professionals tasked with driving AI initiatives. Learners will delve into performance metrics, inter-annotator agreement, and tradeoffs in resources, gaining a nuanced understanding of AI's strengths and limitations. Whether you're a newcomer or looking to deepen your understanding, this course empowers you to make informed AI decisions, optimize systems, and address challenges in data quality and algorithm selection. By the end, you'll have the confidence to navigate AI projects and align them with organizational goals, positioning yourself as a strategic leader in AI-driven innovation.

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

6 videosTotal 120 minutes

Introduction to Artificial Intelligence (AI)39 minutesPreview module
Applications of AI23 minutes
AI Project Management Frameworks14 minutes
R.O.A.D. Framework - Requirements7 minutes
R.O.A.D. Framework - Operationalize Data29 minutes
R.O.A.D. Framework - Analytic Methods & Deployment4 minutes

4 readingsTotal 200 minutes

Readings References60 minutes
Reading References60 minutes
Self-Reflective Reading: AI Project Management Scenario - The SmartGrocery Shopper Assistant40 minutes
Self-Reflective Reading: Reflective Essay40 minutes

3 assignmentsTotal 90 minutes

Exploring AI Fundamentals and Project Management15 minutes
Mastering the R.O.A.D. Framework for AI Projects15 minutes
Introduction to Artificial Intelligence60 minutes

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

6 videosTotal 63 minutes

Steps in Hypothesis Testing14 minutesPreview module
Two Sample T-Testing14 minutes
Analysis of Variance (ANOVA) 1 minute
Types of Error in Statistical Estimation11 minutes
Machine Learning Performance - Part 113 minutes
Machine Learning Performance - Part 26 minutes

2 readingsTotal 20 minutes

Readings References10 minutes
Readings References10 minutes

3 assignmentsTotal 90 minutes

Foundations of Hypothesis Testing and Statistical Analysis15 minutes
Understanding Errors and Performance Metrics in Machine Learning15 minutes
Machine Learning60 minutes

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

8 videosTotal 63 minutes

Algorithm Tradeoffs5 minutesPreview module
Support Vector Machines7 minutes
Naïve Bayes5 minutes
Decision Trees10 minutes
Random Forest4 minutes
Neural Networks14 minutes
Unsupervised Learning2 minutes
Algorithm Tradeoff Scenarios13 minutes

2 readingsTotal 60 minutes

Reading References30 minutes
Readings References30 minutes

3 assignmentsTotal 90 minutes

Exploring Supervised Learning and Algorithm Tradeoffs15 minutes
Advanced Algorithms and Tradeoff Scenarios15 minutes
Algorithm Tradeoffs 60 minutes

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

9 videosTotal 90 minutes

Introduction to Data15 minutesPreview module
Data Examples 2 minutes
Labeling11 minutes
Labeling Challenges6 minutes
Labeling Considerations7 minutes
Cognitive Limitations10 minutes
Inter-Annotator Agreement (IAA)10 minutes
Krippendorf’s Alpha10 minutes
Size-Consistency-Quality Tradeoffs14 minutes

2 readingsTotal 60 minutes

Readings References30 minutes
Reading References30 minutes

3 assignmentsTotal 90 minutes

Data Basics and Labeling Techniques15 minutes
Data Quality and Annotation Standards15 minutes
Data - Assessments60 minutes

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

10 videosTotal 102 minutes

Resources3 minutesPreview module
Memory14 minutes
Memory Applications and Challenges8 minutes
Memory Management Strategies12 minutes
Computational Tradeoffs - Algorithms10 minutes
Computational Tradeoffs - Parallelization13 minutes
Query Expressiveness8 minutes
Query Expressiveness Examples14 minutes
Query Expressiveness - Practical Implications6 minutes
Performance11 minutes

2 readingsTotal 180 minutes

Reading References90 minutes
Reading References90 minutes

3 assignmentsTotal 90 minutes

Memory Management and Computational Tradeoffs in Algorithms15 minutes
Computational Tradeoffs, Query Expressiveness, and Performance Optimization15 minutes
Resources60 minutes