Overview
Plus: (1) AI and Humans, (2) Generative AI and Leaders, (3) AI and Operations, (4) AI and Business Strategy
Syllabus
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- Introduction to AI and Machine Learning
-- Definition and History of AI
-- Key Differences between AI, Machine Learning, and Deep Learning
- Fundamental Concepts of Machine Learning
-- Supervised Learning
-- Unsupervised Learning
-- Reinforcement Learning
- Key Algorithms and Techniques
-- Linear Regression
-- Classification Algorithms (e.g., Decision Trees, SVMs)
-- Clustering (e.g., K-Means)
-- Neural Networks and Deep Learning Basics
- Data Preparation and Feature Engineering
-- Data Cleaning and Preprocessing
-- Feature Selection and Extraction
-- Handling Missing Data
- Model Evaluation and Optimization
-- Training and Test Sets
-- Cross-Validation
-- Evaluation Metrics (e.g., Accuracy, Precision, Recall)
- Practical Applications of AI and ML
-- AI in Business and Industry
-- Use Cases in Marketing, Healthcare, Finance, and more
- Tools and Environments
-- Overview of Popular ML Tools (e.g., Python libraries such as NumPy, pandas, scikit-learn, TensorFlow)
-- Setting up a Development Environment
- Ethical Considerations and Future Trends
-- Bias and Fairness in AI
-- Responsible AI and Privacy Concerns
-- Future Directions in AI Research
- Course Wrap-up and Next Steps
-- Summary of Key Concepts
-- Resources for Further Learning
-- Career Paths in AI and Machine Learning
Taught by
Irlon Terblanche and Peter Alkema
Tags
On-Demand
