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Course Outline
Introduction to Google Colab Pro
- Comparing Colab and Colab Pro: features and limitations.
- Creating and managing notebooks.
- Hardware accelerators and runtime configuration.
Python Programming in the Cloud
- Understanding code cells, markdown, and notebook structure.
- Installing packages and setting up environments.
- Saving and versioning notebooks within Google Drive.
Data Processing and Visualization
- Loading and analyzing data from files, Google Sheets, or APIs.
- Utilizing Pandas, Matplotlib, and Seaborn.
- Streaming and visualizing large datasets.
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow in Colab.
- Training models on GPU/TPU hardware.
- Evaluating and tuning model performance.
Working with Deep Learning Frameworks
- Using PyTorch with Colab Pro.
- Managing memory and runtime resources.
- Saving checkpoints and maintaining training logs.
Integration and Collaboration
- Mounting Google Drive and accessing shared datasets.
- Collaborating through shared notebooks.
- Exporting content to GitHub or PDF for distribution.
Performance Optimization and Best Practices
- Managing session lifetime and handling timeouts.
- Organizing code efficiently within notebooks.
- Tips for executing long-running or production-level tasks.
Summary and Next Steps
Requirements
- Prior experience with Python programming.
- Familiarity with Jupyter notebooks and fundamental data analysis techniques.
- Understanding of standard machine learning workflows.
Audience
- Data scientists and analysts.
- Machine learning engineers.
- Python developers engaged in AI or research initiatives.
14 Hours
