Machine Learning, Artificial Intelligence, and Deep Learning Explained

Übersicht

Explore AI, ML, and DL with Buck Woody:

learn key differences, use-cases, and implementation options, including SQL Server platform technologies.

Lehrplan

• Introduction to AI, ML, and DL
Definitions and Key Concepts
Historical Context and Evolution
Real-world Applications and Use-Cases
• Differences Between AI, ML, and DL
Overview of AI, ML, and DL
Key Distinctions and Overlaps
Practical Scenarios for Each Technology
• Machine Learning (ML) Foundations
Supervised vs. Unsupervised Learning
Key Algorithms: Regression, Classification, Clustering
Model Training, Validation, and Testing
• Artificial Intelligence (AI) in Practice
Rule-based Systems
Intelligent Agents
Applications in Natural Language Processing and Robotics
• Deep Learning (DL) Essentials
Neural Networks Basics
Convolutional Networks and Applications
Recurrent Networks and Sequence Modeling
• Implementation Platforms and Tools
Overview of Popular ML and DL Frameworks
Programming Languages: Python, R, Others
Introduction to SQL Server for AI and ML
• SQL Server Platform Technologies for AI/ML
Machine Learning Services in SQL Server
Integration of R and Python
Building and Deploying Machine Learning Models
• Use-cases and Case Studies
Industry-specific Examples
Successful Implementations
Lessons Learned and Best Practices
• Ethical Considerations and Future Trends
Bias and Fairness in AI
AI and Data Privacy
Emerging Trends and Technologies
• Course Wrap-Up and Next Steps
Recap of Key Learnings
Opportunities for Further Study
Resources and Tools for Continued Learning

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Fachgebiete

Conference Talks