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Duration 14 hours
Course Outline
Overview of Google Colab Pro
- Distinguishing Colab from Colab Pro: features and constraints
- Notebook creation and management processes
- Hardware accelerators and runtime configuration settings
Cloud-Based Python Programming
- Code cells, markdown formatting, and overall notebook architecture
- Installing packages and configuring the development environment
- Storing and version-controlling notebooks via Google Drive
Data Processing and Visualization Techniques
- Ingesting and analyzing data from files, Google Sheets, or external APIs
- Leveraging Pandas, Matplotlib, and Seaborn for analysis
- Processing and visualizing large-scale datasets
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow within the Colab environment
- Model training utilizing GPU/TPU resources
- Model performance evaluation and parameter tuning
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Monitoring memory usage and managing runtime resources
- Saving model checkpoints and training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborative work through shared notebooks
- Exporting results to GitHub or PDF for easy distribution
Performance Optimization and Best Practices
- Managing session duration and preventing timeouts
- Structuring code efficiently within notebooks
- Strategies for handling long-running or production-level tasks
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Experience with Jupyter notebooks and fundamental data analysis techniques
- A solid grasp of standard machine learning workflows
Target Audience
- Data scientists and business analysts
- Machine learning engineers
- Python developers engaged in AI or research initiatives