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Duration 14 hours
Course Outline
Introduction to Google Colab Pro
- Comparison of Colab vs. Colab Pro: features and constraints
- Notebook creation and management techniques
- Hardware accelerators and runtime configuration
Cloud-Based Python Programming
- Code cells, markdown, and notebook architecture
- Installing packages and setting up environments
- Saving and versioning notebooks via Google Drive
Data Processing and Visualization
- Ingesting and analyzing data from files, Google Sheets, or APIs
- Application of Pandas, Matplotlib, and Seaborn
- Streaming and visualizing large-scale datasets
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow in Colab
- Training models using GPU/TPU resources
- Assessing and tuning model performance
Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Managing memory and runtime resources efficiently
- Saving checkpoints and maintaining training logs
Integration and Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborating through shared notebooks
- Exporting work to GitHub or PDF for distribution
Performance Optimization and Best Practices
- Controlling session duration and timeout settings
- Structuring code effectively within notebooks
- Strategies for long-running and production-grade tasks
Conclusion and Future Steps
Requirements
- Proficiency in Python programming
- Familiarity with Jupyter notebooks and fundamental 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
