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.
Introduction to AI: Key Concepts and Applications
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您将学到什么
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.
您将获得的技能
- Model Evaluation
- Artificial Neural Networks
- Data Quality
- Random Forest Algorithm
- Classification Algorithms
- System Requirements
- Machine Learning
- Machine Learning Algorithms
- Responsible AI
- Performance Metric
- Decision Tree Learning
- Model Deployment
- Data Management
- Artificial Intelligence and Machine Learning (AI/ML)
- Resource Utilization
- Strategic Leadership
- Algorithms
- AI Enablement
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作业
15 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有6个模块
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.
涵盖的内容
1篇阅读材料1个插件
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.
涵盖的内容
6个视频3篇阅读材料3个作业
This module covers the statistical foundations of machine learning and the common metrics for evaluating machine learning and artificial intelligence performance.
涵盖的内容
6个视频1篇阅读材料3个作业
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.
涵盖的内容
8个视频1篇阅读材料3个作业
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.
涵盖的内容
9个视频1篇阅读材料3个作业
This module introduces the most common resource considerations in AI, specifically memory, computational tradeoffs, query expressiveness, and algorithm performance.
涵盖的内容
10个视频1篇阅读材料3个作业
位教师
授课教师评分
4.8 (9个评价)
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状态：预览O.P. Jindal Global University
状态：预览University of Illinois Urbana-Champaign
状态：预览
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
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学生评论
4.8
24 条评论
- 5 stars
83.33%
- 4 stars
16.66%
- 3 stars
0%
- 2 stars
0%
- 1 star
0%
显示 3/24 个
AP
5
已于 Feb 20, 2025审阅
Very well structured and very informative, much appreciated.
MG
5
已于 Aug 17, 2025审阅
challenging but interesting if you want to learn more intermediate/advanced things on AI
MS
5
已于 Nov 21, 2025审阅
A very good Introduction To AI. Thank you Dr. Ian McCulloh and thank you Johns Hopkins!!
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