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Course Outline
Introduction to Google Colab Pro
- Comparative analysis: Colab vs. Colab Pro features and constraints
- Notebook creation and management strategies
- Overview of hardware accelerators and runtime configurations
Cloud-Based Python Programming
- Structure of code cells, markdown, and notebooks
- Installing packages and configuring development environments
- Saving and managing notebook versions through Google Drive
Data Processing and Visualization
- Ingesting and analyzing data from files, Google Sheets, and APIs
- Applying Pandas, Matplotlib, and Seaborn for analysis
- Handling and visualizing large-scale datasets
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow within the Colab environment
- Training models leveraging GPU or TPU resources
- Assessing and fine-tuning model performance
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Optimizing memory usage and runtime resource allocation
- Managing checkpoints and training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets
- Facilitating team collaboration through shared notebooks
- Exporting results to GitHub or PDF for sharing
Performance Optimization and Best Practices
- Managing session lifetimes and preventing timeouts
- Organizing code effectively within notebooks
- Strategies for executing long-running or production-grade tasks
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Knowledge of Jupyter notebooks and foundational data analysis techniques
- Basic understanding of standard machine learning workflows
Target Audience
- Data scientists and business analysts
- Machine learning engineers
- Python developers focused on AI or research-based projects
14 Hours