Core Concepts in AI
Master AI fundamentals, from machine learning algorithms to data quality and resource management, bridging technical concepts with strategic execution.
Master AI fundamentals, from machine learning algorithms to data quality and resource management, bridging technical concepts with strategic execution.
This comprehensive course builds a strong foundation in artificial intelligence and machine learning concepts, focusing on practical implementation and strategic decision-making. You'll learn the essential R.O.A.D. Framework (Requirements, Operationalize Data, Analytic Method, Deployment) for effective AI project management and explore key performance metrics to evaluate machine learning models. The course delves into algorithm selection, analyzing strengths and weaknesses of various approaches including Support Vector Machines, Decision Trees, and Neural Networks. You'll develop critical skills in assessing data quality, calculating inter-annotator agreement, and navigating the tradeoffs between computational resources and performance. Designed for both technical and non-technical professionals, this course bridges theoretical concepts with practical applications, empowering you to make informed decisions about AI implementation and align AI initiatives with organizational goals.
Instructors:
English
Not specified
Powered by
What you'll learn
Understand key AI terminology and concepts to communicate effectively in AI projects
Apply the R.O.A.D. Framework to structure and manage AI implementations systematically
Evaluate machine learning models using appropriate performance metrics
Select optimal algorithms based on problem requirements and resource constraints
Assess data quality and calculate inter-annotator agreement for labeled datasets
Identify strengths and weaknesses of different machine learning approaches
Skills you'll gain
This course includes:
12 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
This course provides a comprehensive exploration of artificial intelligence and machine learning fundamentals, carefully balancing technical depth with strategic application. The curriculum begins with an introduction to AI concepts and the practical R.O.A.D. Framework for project management. Students then delve into the statistical foundations of machine learning, covering hypothesis testing and performance metrics. The course examines algorithm tradeoffs across various methods including Support Vector Machines, Naïve Bayes, Decision Trees, Random Forest, and Neural Networks, highlighting their strengths and weaknesses for different problem types. Data quality considerations are thoroughly addressed, with special attention to labeling challenges, cognitive limitations, and inter-annotator agreement measurement. The final module covers essential resource management in AI systems, including memory optimization, computational tradeoffs, and performance considerations. Throughout the course, theoretical concepts are reinforced through practical scenarios and real-world applications, making it ideal for both technical practitioners and decision-makers responsible for AI implementation.
Course Introduction
Module 1 · 9 Minutes to complete
Introduction to Artificial Intelligence
Module 2 · 6 Hours to complete
Machine Learning
Module 3 · 2 Hours to complete
Algorithm Tradeoffs
Module 4 · 3 Hours to complete
Data
Module 5 · 4 Hours to complete
Resources
Module 6 · 6 Hours to complete
Fee Structure
Instructor
Professor or Instructor in Artificial Intelligence and Statistical Methods
Ian McCulloh is associated with Johns Hopkins University and is involved in courses related to artificial intelligence, probability, and statistical methods. His expertise likely spans AI project management, social media analytics, and foundational concepts in AI. He may also be involved in teaching or research related to neuroscience and social computing. If Ian McCulloh is a specific instructor, more detailed information about his background or specific courses taught would be needed to provide a more accurate description.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.