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Course Outline
Introduction to Google Colab Pro for Government
- Comparison of Features and Limitations Between Colab and Colab Pro
- Creating and Managing Notebooks in a Secure Environment
- Utilizing Hardware Accelerators and Configuring Runtime Settings for Enhanced Performance
Python Programming in the Cloud for Government
- Structure and Functionality of Code Cells, Markdown, and Notebooks
- Installing Packages and Setting Up Environments for Secure Data Processing
- Saving and Versioning Notebooks in Google Drive to Ensure Data Integrity
Data Processing and Visualization for Government
- Loading and Analyzing Data from Various Sources, Including Files, Google Sheets, or APIs
- Utilizing Pandas, Matplotlib, and Seaborn for Data Manipulation and Visualization
- Handling and Visualizing Large Datasets to Support Informed Decision-Making
Machine Learning with Colab Pro for Government
- Implementing Scikit-learn and TensorFlow in a Secure, Cloud-Based Environment
- Training Models on GPU/TPU to Accelerate Computational Tasks
- Evaluating and Tuning Model Performance for Optimal Results
Working with Deep Learning Frameworks for Government
- Utilizing PyTorch within the Colab Pro Environment
- Managing Memory and Runtime Resources to Ensure Efficient Operations
- Saving Checkpoints and Training Logs for Continuity and Accountability
Integration and Collaboration for Government
- Mounting Google Drive and Accessing Shared Datasets in a Secure Manner
- Collaborating on Projects Through Shared Notebooks to Enhance Team Productivity
- Exporting Work to GitHub or PDF for Distribution and Reporting
Performance Optimization and Best Practices for Government
- Managing Session Lifetime and Timeouts to Maintain System Stability
- Organizing Code Efficiently in Notebooks to Improve Readability and Maintainability
- Implementing Tips for Long-Running or Production-Level Tasks to Ensure Reliability
Summary and Next Steps for Government
Requirements
- Experience with Python programming for government applications
- Familiarity with Jupyter notebooks and foundational data analysis techniques
- Knowledge of standard machine learning workflows and methodologies
Audience
- Data scientists and analysts in the public sector
- Machine learning engineers for government projects
- Python developers working on AI or research initiatives for government agencies
14 Hours