Introduction to AI: Key Concepts and Applications

4 days left! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Introduction to AI: Key Concepts and Applications

Instructor: Ian McCulloh

3,602 already enrolled

Included with

Learn more

6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

25 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments

Taught in English

91%

of learners achieved a positive career outcome

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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 videos3 readings3 assignments

6 videosTotal 120 minutes

Introduction to Artificial Intelligence (AI)40 minutes
Applications of AI23 minutes
AI Project Management Frameworks14 minutes
R.O.A.D. Framework - Requirements8 minutes
R.O.A.D. Framework - Operationalize Data30 minutes
R.O.A.D. Framework - Analytic Methods & Deployment5 minutes

3 readingsTotal 140 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

Introduction to Artificial Intelligence60 minutes
Exploring AI Fundamentals and Project Management15 minutes
Mastering the R.O.A.D. Framework for AI Projects15 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 videos1 reading3 assignments

6 videosTotal 63 minutes

Steps in Hypothesis Testing15 minutes
Two Sample T-Testing15 minutes
Analysis of Variance (ANOVA) 2 minutes
Types of Error in Statistical Estimation12 minutes
Machine Learning Performance - Part 113 minutes
Machine Learning Performance - Part 26 minutes

1 readingTotal 10 minutes

Readings References10 minutes

3 assignmentsTotal 90 minutes

Machine Learning60 minutes
Foundations of Hypothesis Testing and Statistical Analysis15 minutes
Understanding Errors and Performance Metrics in Machine Learning15 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 videos1 reading3 assignments

8 videosTotal 64 minutes

Algorithm Tradeoffs5 minutes
Support Vector Machines7 minutes
Naïve Bayes5 minutes
Decision Trees10 minutes
Random Forest4 minutes
Neural Networks15 minutes
Unsupervised Learning3 minutes
Algorithm Tradeoff Scenarios13 minutes

1 readingTotal 30 minutes

Reading References30 minutes

3 assignmentsTotal 90 minutes

Algorithm Tradeoffs 60 minutes
Exploring Supervised Learning and Algorithm Tradeoffs15 minutes
Advanced Algorithms and Tradeoff Scenarios15 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 videos1 reading3 assignments

9 videosTotal 90 minutes

Introduction to Data16 minutes
Data Examples 2 minutes
Labeling11 minutes
Labeling Challenges7 minutes
Labeling Considerations7 minutes
Cognitive Limitations11 minutes
Inter-Annotator Agreement (IAA)11 minutes
Krippendorf’s Alpha11 minutes
Size-Consistency-Quality Tradeoffs15 minutes

1 readingTotal 30 minutes

Reading References30 minutes

3 assignmentsTotal 90 minutes

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

This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.

What's included

10 videos1 reading3 assignments

10 videosTotal 102 minutes

Resources3 minutes
Memory15 minutes
Memory Applications and Challenges8 minutes
Memory Management Strategies12 minutes
Computational Tradeoffs - Algorithms10 minutes
Computational Tradeoffs - Parallelization14 minutes
Query Expressiveness9 minutes
Query Expressiveness Examples14 minutes
Query Expressiveness - Practical Implications6 minutes
Performance11 minutes

1 readingTotal 90 minutes

Reading References90 minutes

3 assignmentsTotal 90 minutes

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

Instructor

Instructor ratings

(10 ratings)

Ian McCulloh

Johns Hopkins University

17 Courses28,170 learners

Offered by

Johns Hopkins University

Explore more from Data Management

IBM
Introduction to Artificial Intelligence (AI)
Course
O.P. Jindal Global University
Artificial Intelligence
Course
University of Illinois Urbana-Champaign
Introduction to Artificial Intelligence
Course
Arm
Introduction to AI
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
84%
4 stars
16%
3 stars
0%
2 stars
0%
1 star
0%

Showing 3 of 25

Reviewed on Feb 20, 2025

Very well structured and very informative, much appreciated.

Reviewed on Nov 21, 2025

A very good Introduction To AI. Thank you Dr. Ian McCulloh and thank you Johns Hopkins!!

Reviewed on Aug 17, 2025

challenging but interesting if you want to learn more intermediate/advanced things on AI

View more reviews

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.