中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
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
- Data Validation
- Data Quality
- Responsible AI
- Strategic Leadership
- Artificial Neural Networks
- Artificial Intelligence
- Machine Learning
- Performance Metric
- Algorithms
- Decision Tree Learning
- System Requirements
- Strategic Decision-Making
- Applied Machine Learning
- Resource Utilization
- Random Forest Algorithm
- MLOps (Machine Learning Operations)
要了解的详细信息
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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4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2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2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2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2篇阅读材料3个作业
位教师
授课教师评分
4.8 (6个评价)
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状态：预览University of Illinois Urbana-Champaign
状态：预览
状态：预览Rutgers the State University of New Jersey
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学生评论
4.8
20 条评论
- 5 stars
80%
- 4 stars
20%
- 3 stars
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AP
5
已于 Feb 20, 2025审阅
Very well structured and very informative, much appreciated.
RS
4
已于 Apr 21, 2025审阅
Information was very good but was definitely not an introduction course. Recommend knowledge in statistics and algorithms prior to this course.
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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.
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