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 Duration 14 hours

Course Outline

Getting Started with Google Colab Pro

  • Comparing Colab and Colab Pro: Key features and constraints
  • Notebook creation and management strategies
  • Configuring hardware accelerators and runtime parameters

Cloud-Based Python Development

  • Managing code cells, markdown, and notebook architecture
  • Installing packages and configuring the environment
  • Versioning and storing notebooks via 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

Implementing Machine Learning with Colab Pro

  • Applying Scikit-learn and TensorFlow within Colab
  • Training models utilizing GPU or TPU resources
  • Assessing and refining model performance

Utilizing Deep Learning Frameworks

  • Integrating PyTorch with Colab Pro
  • Overseeing memory usage and runtime resources
  • Managing checkpoints and training logs

Integration and Team Collaboration

  • Mounting Google Drive and accessing shared datasets
  • Collaborating through shared notebook environments
  • Exporting content to GitHub or PDF for distribution

Performance Tuning and Best Practices

  • Controlling session duration and timeout settings
  • Structuring code efficiently within notebooks
  • Recommendations for long-duration or production-grade tasks

Conclusion and Further Development

Requirements

  • Proficiency in Python programming.
  • Working knowledge of Jupyter notebooks and fundamental data analysis techniques.
  • Familiarity with standard machine learning processes.

Target Audience

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

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