What will you learn in this Introduction to AI: Key Concepts and Applications Course
Understand core AI and machine learning (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 Support Vector Machines (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
Program Overview
1. Course Introduction
⏳ 9 minutes
Provides an overview of the course structure, objectives, and introduces the instructor.
2. Introduction to Artificial Intelligence
⏳ 6 hours
Covers fundamental AI concepts, applications, and introduces the R.O.A.D. Framework for AI project management.
3. Machine Learning
⏳ 2 hours
Delves into statistical foundations of ML, performance metrics, and evaluation techniques.
4. Algorithm Tradeoffs
⏳ 3 hours
Explores common AI algorithms, their tradeoffs, and suitability for various problem types.
5. Data
⏳ 4 hours
Focuses on data types, labeling challenges, and the importance of data quality in AI systems.
6. Capstone Project
⏳ 8 hours
Applies learned concepts to a real-world scenario, reinforcing understanding through practical application.
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Job Outlook
Prepares learners for roles such as AI Project Manager, Data Analyst, and Business Intelligence Analyst.
Applicable in industries like technology, healthcare, finance, and manufacturing.
Enhances employability by providing practical skills in AI project management and data analysis.
Supports career advancement in fields requiring expertise in AI strategy and implementation.
Specification: Introduction to AI: Key Concepts and Applications
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FAQs
- A foundational understanding of statistics and linear algebra is helpful.
- Advanced mathematics is not strictly required for basic concepts.
- Concepts are explained with practical examples and intuitive frameworks.
- Additional study may be needed for complex algorithm derivations.
- Beginners can still follow the course if they focus on conceptual understanding.
- Teaches the R.O.A.D. Framework for AI project implementation.
- Covers evaluating algorithms and performance metrics for informed decisions.
- Provides insights into data quality, trade-offs, and project planning.
- Helps in communicating technical concepts to non-technical stakeholders.
- Useful for project managers, business analysts, and AI strategists.
- Includes hands-on exercises with SVM, Decision Trees, and Neural Networks.
- Allows experimentation with real datasets to understand model performance.
- Capstone project reinforces learning in a practical scenario.
- Focuses on conceptual understanding alongside application.
- Supports skill-building for data analysis and AI-driven decision-making.
- Self-paced learning allows completion alongside full-time jobs.
- Modules vary from short introductions to 8-hour capstone projects.
- Hands-on exercises can be done incrementally.
- Lifetime access enables review and reinforcement anytime.
- Suitable for professionals looking to upskill in AI without leaving work.
- Prepares for roles like AI Project Manager, Data Analyst, or Business Intelligence Analyst.
- Skills are applicable in technology, finance, healthcare, and manufacturing industries.
- Provides foundation for AI strategy and implementation roles.
- Enhances employability with practical AI project management experience.
- Supports career advancement in data-driven decision-making positions.
