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 Duration 14 hours

Course Outline

Overview of Google Colab Pro

  • Comparison of Colab and Colab Pro: capabilities and constraints
  • Notebook creation and management techniques
  • Hardware accelerators and runtime configuration

Cloud-Based Python Programming

  • Structure of code cells, markdown, and notebooks
  • Installing packages and setting up the environment
  • Saving and version-controlling notebooks on Google Drive

Data Analysis and Visualization

  • Ingesting and examining data from files, Google Sheets, or APIs
  • Application of Pandas, Matplotlib, and Seaborn
  • Processing and visualizing large-scale datasets

Machine Learning via Colab Pro

  • Implementation of Scikit-learn and TensorFlow in Colab
  • Model training utilizing GPU/TPU resources
  • Assessing and optimizing model performance

Deep Learning Frameworks Integration

  • Working with PyTorch within Colab Pro
  • Oversight of memory and runtime resources
  • Storing checkpoints and training logs

Integration and Team Collaboration

  • Accessing Google Drive and importing shared datasets
  • Collaborative work through shared notebooks
  • Exporting outputs to GitHub or PDF for distribution

Performance Enhancement and Best Practices

  • Handling session duration and timeout settings
  • Organizing code efficiently within notebooks
  • Strategies for long-duration or production-grade tasks

Recap and Future Directions

Requirements

  • Proficiency in Python programming
  • Knowledge of Jupyter notebooks and foundational data analysis
  • Understanding of standard machine learning workflows

Target Audience

  • Data scientists and analysts
  • Machine learning engineers
  • Python developers focused on AI or research initiatives

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