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Duration 14 hours
Course Outline
Overview of Google Colab Pro
- Comparative analysis of Colab and Colab Pro: capabilities and constraints
- Notebook creation and administration
- Configuration of hardware accelerators and runtime parameters
Cloud-Based Python Development
- Structure of code cells, markdown, and notebooks
- Installing packages and configuring the development environment
- Storing and managing notebook versions via Google Drive
Data Handling and Visualization Techniques
- Ingesting and examining data from files, Google Sheets, or API endpoints
- Leveraging Pandas, Matplotlib, and Seaborn libraries
- Processing and visualizing extensive datasets
Machine Learning Applications in Colab Pro
- Implementing Scikit-learn and TensorFlow within Colab
- Model training on GPU/TPU hardware
- Assessing and refining model efficiency
Utilizing Deep Learning Frameworks
- Working with PyTorch in a Colab Pro environment
- Optimization of memory usage and runtime resources
- Preservation of checkpoints and training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared data collections
- Teamwork through shared notebook interfaces
- Distribution via export to GitHub or PDF formats
Performance Enhancement and Best Practices
- Control of session duration and timeout settings
- Structuring code effectively within notebooks
- Strategies for long-duration or production-grade tasks
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Working knowledge of Jupyter notebooks and fundamental data analysis techniques
- Grasp of standard machine learning processes
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives
