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

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