Introduction to AI: Key Concepts and Applications

Introduction to AI: Key Concepts and Applications

Instructor: Ian McCulloh

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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

Model Deployment

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Assessments

15 assignments

Taught in English

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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 videos Total 120 minutes

Introduction to Artificial Intelligence (AI) 40 minutes
Applications of AI 23 minutes
AI Project Management Frameworks 14 minutes
R.O.A.D. Framework - Requirements 8 minutes
R.O.A.D. Framework - Operationalize Data 30 minutes
R.O.A.D. Framework - Analytic Methods & Deployment 5 minutes

3 readings Total 140 minutes

Reading References 60 minutes
Self-Reflective Reading: AI Project Management Scenario - The SmartGrocery Shopper Assistant 40 minutes
Self-Reflective Reading: Reflective Essay 40 minutes

3 assignments Total 90 minutes

Exploring AI Fundamentals and Project Management 15 minutes
Mastering the R.O.A.D. Framework for AI Projects 15 minutes
Introduction to Artificial Intelligence 60 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 videos Total 63 minutes

Steps in Hypothesis Testing 15 minutes
Two Sample T-Testing 15 minutes
Analysis of Variance (ANOVA) 2 minutes
Types of Error in Statistical Estimation 12 minutes
Machine Learning Performance - Part 1 13 minutes
Machine Learning Performance - Part 2 6 minutes

1 reading Total 10 minutes

Readings References 10 minutes

3 assignments Total 90 minutes

Foundations of Hypothesis Testing and Statistical Analysis 15 minutes
Understanding Errors and Performance Metrics in Machine Learning 15 minutes
Machine Learning 60 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 videos Total 64 minutes

Algorithm Tradeoffs 5 minutes
Support Vector Machines 7 minutes
Naïve Bayes 5 minutes
Decision Trees 10 minutes
Random Forest 4 minutes
Neural Networks 15 minutes
Unsupervised Learning 3 minutes
Algorithm Tradeoff Scenarios 13 minutes

1 reading Total 30 minutes

Reading References 30 minutes

3 assignments Total 90 minutes

Exploring Supervised Learning and Algorithm Tradeoffs 15 minutes
Advanced Algorithms and Tradeoff Scenarios 15 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 videos1 reading3 assignments

9 videos Total 90 minutes

Introduction to Data 16 minutes
Data Examples 2 minutes
Labeling 11 minutes
Labeling Challenges 7 minutes
Labeling Considerations 7 minutes
Cognitive Limitations 11 minutes
Inter-Annotator Agreement (IAA) 11 minutes
Krippendorf’s Alpha 11 minutes
Size-Consistency-Quality Tradeoffs 15 minutes

1 reading Total 30 minutes

Reading References 30 minutes

3 assignments Total 90 minutes

Data Basics and Labeling Techniques 15 minutes
Data Quality and Annotation Standards 15 minutes
Data - Assessments 60 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 videos Total 102 minutes

Resources 3 minutes
Memory 15 minutes
Memory Applications and Challenges 8 minutes
Memory Management Strategies 12 minutes
Computational Tradeoffs - Algorithms 10 minutes
Computational Tradeoffs - Parallelization 14 minutes
Query Expressiveness 9 minutes
Query Expressiveness Examples 14 minutes
Query Expressiveness - Practical Implications 6 minutes
Performance 11 minutes

1 reading Total 90 minutes

Reading References 90 minutes

3 assignments Total 90 minutes

Memory Management and Computational Tradeoffs in Algorithms 15 minutes
Computational Tradeoffs, Query Expressiveness, and Performance Optimization 15 minutes
Resources 60 minutes