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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 techniques
  • Configuring hardware accelerators and runtime parameters

Python Development in a Cloud Environment

  • Structuring code cells, markdown, and notebooks
  • Installing packages and setting up the environment
  • Storing and versioning notebooks using 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

Machine Learning with Colab Pro

  • Implementing Scikit-learn and TensorFlow within Colab
  • Training models using GPU/TPU resources
  • Assessing and fine-tuning model performance

Utilizing Deep Learning Frameworks

  • Working with PyTorch in Colab Pro
  • Optimizing 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 sessions
  • Exporting projects to GitHub or PDF for distribution

Performance Tuning and Best Practices

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

Recap and Future Steps

Requirements

  • Proficiency in Python programming
  • Experience with Jupyter notebooks and fundamental data analysis
  • A solid grasp of 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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