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Duration 14 hours
Course Outline
Getting Started with Google Colab Pro
- Comparing Colab and Colab Pro: key features and constraints
- Creating and managing notebook projects
- Configuring hardware accelerators and runtime parameters
Cloud-Based Python Programming
- Understanding code cells, markdown, and notebook architecture
- Installing packages and setting up development environments
- Storing and versioning notebooks via Google Drive
Data Manipulation and Visualization
- Ingesting and analyzing data from files, Google Sheets, or API endpoints
- Applying Pandas, Matplotlib, and Seaborn for data insights
- Processing and visualizing large-scale datasets
Machine Learning via Colab Pro
- Implementing Scikit-learn and TensorFlow models in Colab
- Training models leveraging GPU/TPU resources
- Assessing and refining model performance
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Optimizing memory usage and runtime resources
- Saving model checkpoints and training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborating through shared notebook sessions
- Exporting projects to GitHub or PDF for wider distribution
Performance Tuning and Best Practices
- Managing session duration and timeout settings
- Structuring code efficiently within notebooks
- Strategies for long-running or production-grade tasks
Recap and Future Directions
Requirements
- Prior experience with Python programming.
- Proficiency with Jupyter notebooks and basic data analysis techniques.
- A solid understanding of standard machine learning workflows.
Target Audience
- Data scientists and analysts.
- Machine learning engineers.
- Python developers engaged in AI or research initiatives.
