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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 engaged in AI or research projects.
14 Hours
