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Duration 14 hours
Course Outline
Getting Started with Google Colab Pro
- Comparing Colab and Colab Pro: key features and constraints
- Notebook creation and management techniques
- Configuring hardware accelerators and runtime parameters
Python Development in a Cloud Environment
- Structuring code cells, markdown, and notebooks
- Installing packages and setting up the environment
- Storing and versioning notebooks using Google Drive
Data Handling 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
- Working with PyTorch in Colab Pro
- Optimizing memory usage and runtime resources
- Managing 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 distribution
Performance Tuning and Best Practices
- Overseeing session duration and timeout settings
- Organizing code efficiently within notebooks
- Strategies for managing long-running or production-grade tasks
Recap and Future Steps
Requirements
- Proficiency in Python programming
- Experience with Jupyter notebooks and fundamental data analysis
- A solid grasp of standard machine learning processes
Target Audience
- Data scientists and analysts
- Machine learning engineers
- Python developers focused on AI or research initiatives