掌握 Python 和生成式人工智能，实现高级分析
Master Python and Generative Ai For Advanced Analytics
- 1 - Course Outline and Goal
- 2 - Introduction to Generative AI
- 3 - Applications in Advanced Analytics
- 4 - Different types of Generative Models
- 5 - Generative AI vs Traditional ML
- 6 - Course Structure and Learning Objectives
- 7 - Python for Generative AI Workflows
- 8 - Setting Up the Environment
- 9 - Variables and Data types
- 10 - Data Structures in Python
- 11 - Control flows in python
- 12 - Functions in Python
- 13 - Object Oriented Programming in Python
- 14 - Regular Expressions in Python
- 15 - Modules in Python
- 16 - File Handling in Python
- 17 - Error Handling in Python
- 18 - Essential Python Libraries for Generative AITheory
- 19 - Data manipulation
- 20 - Data visualization
- 21 - Image Processing
- 22 - Machine Learning tools
- 23 - Model Building and Training
- 24 - Data Wrangling for Python Part 1
- 25 - Data Wrangling for Python Part 2
- 26 - Advanced Python Concepts
- 27 - Generative AI Libraries
- 28 - Understanding Generative Adversarial Networks
- 29 - Constructing Your First GAN with Python
- 30 - Model Training and Optimization Techniques
- 31 - Troubleshooting Training Challenges
- 32 - Understanding Model Performance
- 33 - Data Generation
- 34 - Augmentation for Improved Analysis
- 35 - Advanced Text Analysis with Generative AI
- 36 - Generative AI for Images Signals
- 37 - 75Predictive Analytics with Generative AI
- 38 - Analytics Insights with Generative AI
- 39 - Applications of Generative AI in Advanced Analytics
- 40 - 81Data Collection Preprocessing
- 41 - 82Model Building
- 42 - 83Data Generation Trend Analysis
- 43 - Evaluation
- 44 - Course Recap and Key Learnings
- 45 - The Future of Generative AI and Impact on Advanced Analytics
- 46 - Additional Resources and Learning Paths