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Duration 14 hours
Course Outline
Introduction to Google Colab Pro
- Differentiating Colab and Colab Pro: capabilities and constraints
- Notebook creation and administration
- Hardware accelerators and runtime configurations
Python Programming in the Cloud
- Code cells, markdown, and notebook architecture
- Installing packages and configuring the environment
- Saving and versioning notebooks on Google Drive
Data Processing and Visualization
- Ingesting and analyzing data from files, Google Sheets, or APIs
- Applying Pandas, Matplotlib, and Seaborn for analysis
- Streaming and visualizing large-scale datasets
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow within Colab
- Training models utilizing GPU/TPU resources
- Assessing and optimizing model performance
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Managing memory usage and runtime resources
- Saving checkpoints and training logs
Integration and Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborating through shared notebook interfaces
- Exporting to GitHub or PDF for distribution
Performance Optimization and Best Practices
- Managing session duration and timeout settings
- Structuring code efficiently within notebooks
- Strategies for long-running or production-grade tasks
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Working knowledge of Jupyter notebooks and fundamental data analysis
- Conceptual understanding of standard machine learning workflows
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives
