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Duration 14 hours
Course Outline
Overview of Google Colab Pro
- Comparing Colab and Colab Pro: key features and constraints
- Notebook creation and administrative management
- Configuration of hardware accelerators and runtime parameters
Cloud-Centric Python Development
- Managing code cells, markdown, and overall notebook architecture
- Installing packages and configuring the development environment
- Notebook persistence and version control via Google Drive
Data Handling and Visual Analysis
- Ingesting and examining data from files, Google Sheets, or external APIs
- Application of Pandas, Matplotlib, and Seaborn
- Processing and visualizing extensive data sets
Machine Learning via Colab Pro
- Integrating Scikit-learn and TensorFlow within the Colab ecosystem
- Model training utilizing GPU/TPU acceleration
- Performance evaluation and hyperparameter tuning
Advanced Deep Learning Implementations
- Leveraging PyTorch in conjunction with Colab Pro
- Optimization of memory usage and runtime resource allocation
- Saving model checkpoints and logging training progress
Integration and Team Collaboration
- Mounting Google Drive and accessing shared data resources
- Facilitating teamwork through shared notebook instances
- Exporting content to GitHub or PDF for broader distribution
Performance Tuning and Operational Best Practices
- Managing session persistence and timeout settings
- Structuring notebook code for efficiency and readability
- Strategies for handling long-duration and production-grade tasks
Conclusion and Future Pathways
Requirements
- Proficiency in Python coding
- Working knowledge of Jupyter notebooks and fundamental data analysis
- General grasp of standard machine learning processes
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives
