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Duration 14 hours
Course Outline
Introduction to Google Colab Pro
- Comparing Colab and Colab Pro: key features and limitations
- Notebook creation and management
- Hardware accelerators and runtime configurations
Cloud-Based Python Programming
- Understanding code cells, markdown, and notebook architecture
- Package installation and environment configuration
- Notebook saving and version control via Google Drive
Data Processing and Visualization
- Ingesting and analyzing data from files, Google Sheets, or APIs
- Leveraging Pandas, Matplotlib, and Seaborn
- Processing and visualizing large-scale datasets
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow within Colab
- Training models using GPU/TPU resources
- Assessing and fine-tuning model performance
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Optimizing memory and runtime resource usage
- Saving checkpoints and monitoring training logs
Integration and Collaboration
- Connecting Google Drive and accessing shared datasets
- Team collaboration through shared notebooks
- Exporting projects to GitHub or PDF for wider distribution
Performance Optimization and Best Practices
- Managing session duration and timeout settings
- Structuring code efficiently within notebooks
- Strategies for long-running or production-level tasks
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Working knowledge of Jupyter notebooks and fundamental data analysis techniques
- Conceptual understanding of standard machine learning workflows
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives
