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Duration 14 hours
Course Outline
Overview of Google Colab Pro
- Comparing Colab and Colab Pro: key features and constraints
- Creating and organizing notebooks
- Configuring hardware accelerators and runtime parameters
Cloud-Based Python Development
- Structure of code cells, markdown, and notebooks
- Installing packages and setting up environments
- Version control and storage of notebooks on Google Drive
Data Handling and Visualization
- Ingesting and analyzing data from files, Google Sheets, or API endpoints
- Applying Pandas, Matplotlib, and Seaborn for analysis
- Processing and visualizing extensive datasets
Machine Learning via Colab Pro
- Implementing Scikit-learn and TensorFlow within Colab
- Training models utilizing GPU/TPU resources
- Assessing and fine-tuning model accuracy
Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Managing memory usage and runtime allocations
- Saving model checkpoints and training records
Integration and Team Collaboration
- Accessing Google Drive and importing shared datasets
- Working together through shared notebook instances
- Exporting outputs to GitHub or PDF for sharing
Optimization and Best Practices
- Controlling session duration and preventing timeouts
- Structuring code efficiently within notebooks
- Strategies for long-duration or production-grade tasks
Recap and Future Directions
Requirements
- Practical experience with Python programming
- Proficiency in Jupyter notebooks and fundamental data analysis techniques
- A solid grasp of standard machine learning processes
Target Audience
- Data scientists and business analysts
- Machine learning engineers
- Python developers engaged in AI initiatives or research projects
