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Duration 14 hours
Course Outline
Getting Started with Google Colab Pro
- Colab vs. Colab Pro: Key features and constraints.
- Notebook creation and management strategies.
- Hardware accelerators and runtime configuration.
Cloud-Based Python Programming
- Notebook structure: Code cells and Markdown.
- Installing packages and setting up the environment.
- Storing and versioning notebooks within 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
- Applying Scikit-learn and TensorFlow in the Colab environment.
- Model training on GPU/TPU resources.
- Assessing and refining model performance.
Deep Learning Frameworks
- Integrating PyTorch with Colab Pro.
- Optimizing memory and runtime resource usage.
- Managing checkpoints and training logs.
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets.
- Collaborative work through shared notebooks.
- Exporting content to GitHub or PDF for sharing.
Performance Optimization and Best Practices
- Controlling session lifetimes and timeout settings.
- Organizing code effectively within notebooks.
- Best practices for long-running or production-grade tasks.
Recap and Future Directions
Requirements
- Proficiency in Python programming.
- Familiarity with Jupyter notebooks and foundational data analysis.
- A solid grasp of common machine learning workflows.
Target Audience
- Data scientists and analysts.
- Machine learning engineers.
- Python developers engaged in AI or research initiatives.
