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 Duration 14 hours

Course Outline

Getting Started with Google Colab Pro

  • Colab vs. Colab Pro: comparing features and usage limits
  • Notebook creation and management strategies
  • Hardware accelerators and runtime configuration

Cloud-Based Python Development

  • Structuring notebooks with code cells and markdown
  • Installing packages and configuring the development environment
  • Saving and version controlling notebooks via Google Drive

Data Handling and Visualization Techniques

  • Ingesting and analyzing data from files, Google Sheets, or APIs
  • Leveraging Pandas, Matplotlib, and Seaborn for analysis
  • Processing and visualizing large-scale datasets

Implementing Machine Learning with Colab Pro

  • Applying Scikit-learn and TensorFlow within the Colab environment
  • Training models utilizing GPU/TPU resources
  • Assessing and optimizing model performance

Deep Learning Framework Integration

  • Working with PyTorch in Google Colab Pro
  • Monitoring and managing memory and runtime resources
  • Saving checkpoints and maintaining training logs

Integration and Team Collaboration

  • Mounting Google Drive and accessing shared datasets
  • Enhancing teamwork through shared notebooks
  • Exporting projects to GitHub or PDF for distribution

Performance Tuning and Best Practices

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

Wrap-Up and Recommended Next Steps

Requirements

  • Proficiency in Python programming
  • Working knowledge of Jupyter notebooks and fundamental data analysis techniques
  • Basic understanding of standard machine learning workflows

Intended Audience

  • Data scientists and analysts
  • Machine learning engineers
  • Python developers engaged in AI or research initiatives

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