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Duration 14 hours
Course Outline
Getting Started with Google Colab Pro
- Colab vs. Colab Pro: comparing features and usage limits
- Notebook creation and management strategies
- Hardware accelerators and runtime configuration
Cloud-Based Python Development
- Structuring notebooks with code cells and markdown
- Installing packages and configuring the development environment
- Saving and version controlling notebooks via Google Drive
Data Handling and Visualization Techniques
- Ingesting and analyzing data from files, Google Sheets, or APIs
- Leveraging Pandas, Matplotlib, and Seaborn for analysis
- Processing and visualizing large-scale datasets
Implementing Machine Learning with Colab Pro
- Applying Scikit-learn and TensorFlow within the Colab environment
- Training models utilizing GPU/TPU resources
- Assessing and optimizing model performance
Deep Learning Framework Integration
- Working with PyTorch in Google Colab Pro
- Monitoring and managing memory and runtime resources
- Saving checkpoints and maintaining training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets
- Enhancing teamwork through shared notebooks
- Exporting projects to GitHub or PDF for distribution
Performance Tuning and Best Practices
- Controlling session longevity and timeout settings
- Organizing code efficiently within notebooks
- Strategies for long-duration or production-grade tasks
Wrap-Up and Recommended Next Steps
Requirements
- Proficiency in Python programming
- Working knowledge of Jupyter notebooks and fundamental data analysis techniques
- Basic understanding of standard machine learning workflows
Intended Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives