A Behind the Scenes Look at ML, AI, and Deep Learning

Overview

Explore the mechanics of ML, AI, and Deep Learning, understanding their differences and inner workings. Gain fundamental knowledge to support decision-making and organizational AI initiatives.

Syllabus

• Introduction to AI, ML, and Deep Learning
Definitions and Key Concepts
Historical Overview and Evolution
Why AI, ML, and Deep Learning Matter
• Machine Learning: Foundations and Applications
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Algorithms and Techniques
Evaluating and Validating ML Models
• Deep Learning: An In-depth Understanding
Neural Networks: Architecture and Types
Training Deep Neural Networks
Convolutional and Recurrent Neural Networks
• Artificial Intelligence: Beyond Machine Learning
Symbolic AI vs. Connectionist AI
Expert Systems and Rule-based Systems
Natural Language Processing and Computer Vision
• Tools and Frameworks
Overview of Popular AI and ML Libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Platforms for Model Deployment and Management
• Real-world Applications and Case Studies
AI in Various Industries (Healthcare, Finance, Retail, etc.)
Ethical Considerations and AI Governance
Building AI Strategy for Organizations
• Hands-on Workshop: Building a Simple ML Model
Data Preprocessing and Feature Engineering
Model Development and Training
Model Evaluation and Interpretation
• Assessing Organizational Needs for AI Initiatives
Identifying Opportunities and Challenges
Aligning AI Projects with Business Goals
• Future Trends in AI and Deep Learning
Emerging Technologies and Innovations
The Role of AI in Digital Transformation
• Conclusion and Next Steps
Resources for Further Learning
Initiating AI Projects and Continuous Improvement

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Subjects

Conference Talks