AI Fundamentals for Data Professionals

Introduction

• Introduction

1. Introduction to AI and Machine Learning

• AI and machine learning overview
• Types of machine learning
• Popular AI and ML tools
• AI applications and use cases

2. Data and Feature Engineering

• Data types and sources
• Exploratory data analysis (EDA)
• Data preprocessing techniques
• Feature engineering

3. High-Level Machine Learning Techniques

• Supervised learning overview
• Unsupervised learning overview
• Reinforcement learning overview
• Deep learning overview: Part 1
• Deep learning overview: Part 2

4. Implementation and Future Trends

• Model evaluation and validation
• AI project lifecycle
• AI ethics and bias: Part 1
• AI ethics and bias: Part 2

Conclusion

• Bring it all together