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Duration 14 hours
Course Outline
Introduction to Google Colab Pro
- Comparing Colab and Colab Pro: features and constraints
- Creating and managing notebooks
- Hardware accelerators and runtime configuration
Python Programming in the Cloud
- Code cells, markdown formatting, and notebook architecture
- Package installation and environment setup
- Saving and versioning notebooks within Google Drive
Data Processing and Visualization
- Importing and analyzing data from files, Google Sheets, or APIs
- Leveraging Pandas, Matplotlib, and Seaborn
- Streaming and visualizing extensive datasets
Machine Learning with Colab Pro
- Utilizing Scikit-learn and TensorFlow in Colab
- Model training on GPU/TPU hardware
- Assessing and optimizing model performance
Working with Deep Learning Frameworks
- Using PyTorch with Colab Pro
- Managing memory allocation and runtime resources
- Saving checkpoints and training logs
Integration and Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborating through shared notebooks
- Exporting to GitHub or PDF for distribution
Performance Optimization and Best Practices
- Managing session duration and timeouts
- Organizing code efficiently within notebooks
- Strategies for long-running or production-grade tasks
Summary and Next Steps
Requirements
- Proficiency in Python programming. <\/li>
- Competence with Jupyter notebooks and fundamental data analysis techniques. <\/li>
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A solid grasp of standard machine learning processes.
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Target Audience <\/p>
- Data scientists and analysts. <\/li>
- Machine learning engineers. <\/li>
- Python developers engaged in AI initiatives or research endeavors. <\/li> <\/ul>
