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Duration 14 hours
Course Outline
Overview of Google Colab Pro
- Comparison of Colab and Colab Pro: capabilities and constraints
- Notebook creation and management techniques
- Hardware accelerators and runtime configuration
Cloud-Based Python Programming
- Structure of code cells, markdown, and notebooks
- Installing packages and setting up the environment
- Saving and version-controlling notebooks on Google Drive
Data Analysis and Visualization
- Ingesting and examining data from files, Google Sheets, or APIs
- Application of Pandas, Matplotlib, and Seaborn
- Processing and visualizing large-scale datasets
Machine Learning via Colab Pro
- Implementation of Scikit-learn and TensorFlow in Colab
- Model training utilizing GPU/TPU resources
- Assessing and optimizing model performance
Deep Learning Frameworks Integration
- Working with PyTorch within Colab Pro
- Oversight of memory and runtime resources
- Storing checkpoints and training logs
Integration and Team Collaboration
- Accessing Google Drive and importing shared datasets
- Collaborative work through shared notebooks
- Exporting outputs to GitHub or PDF for distribution
Performance Enhancement and Best Practices
- Handling session duration and timeout settings
- Organizing code efficiently within notebooks
- Strategies for long-duration or production-grade tasks
Recap and Future Directions
Requirements
- Proficiency in Python programming
- Knowledge of Jupyter notebooks and foundational data analysis
- Understanding of standard machine learning workflows
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers focused on AI or research initiatives