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Course Outline
Introduction to Google Colab Pro
- Colab vs. Colab Pro: features and limitations
- Creating and managing notebooks
- Hardware accelerators and runtime settings
Python Programming in the Cloud
- Code cells, markdown, and notebook structure
- Package installation and environment setup
- Saving and versioning notebooks in Google Drive
Data Processing and Visualization
- Loading and analyzing data from files, Google Sheets, or APIs
- Using Pandas, Matplotlib, and Seaborn
- Streaming and visualizing large datasets
Machine Learning with Colab Pro
- Using Scikit-learn and TensorFlow in Colab
- Training models on GPU/TPU
- Evaluating and tuning model performance
Working with Deep Learning Frameworks
- Using PyTorch with Colab Pro
- Managing memory and runtime resources
- Saving checkpoints and training logs
Integration and Collaboration
- Mounting Google Drive and loading shared datasets
- Collaborating via shared notebooks
- Exporting to GitHub or PDF for distribution
Performance Optimization and Best Practices
- Managing session lifetime and timeouts
- Efficient code organization in notebooks
- Tips for long-running or production-level tasks
Summary and Next Steps
Requirements
- Experience with Python programming
- Familiarity with Jupyter notebooks and basic data analysis
- An understanding of common machine learning workflows
Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers working on AI or research projects
14 Hours
