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Course Outline

Introduction to Google Colab Pro

  • Comparing Colab and Colab Pro: features and constraints
  • Creating and managing notebooks
  • Hardware accelerators and runtime configuration

Python Programming in the Cloud

  • Code cells, markdown formatting, and notebook architecture
  • Package installation and environment setup
  • Saving and versioning notebooks within Google Drive

Data Processing and Visualization

  • Importing and analyzing data from files, Google Sheets, or APIs
  • Leveraging Pandas, Matplotlib, and Seaborn
  • Streaming and visualizing extensive datasets

Machine Learning with Colab Pro

  • Utilizing Scikit-learn and TensorFlow in Colab
  • Model training on GPU/TPU hardware
  • Assessing and optimizing model performance

Working with Deep Learning Frameworks

  • Using PyTorch with Colab Pro
  • Managing memory allocation and runtime resources
  • Saving checkpoints and training logs

Integration and Collaboration

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

Performance Optimization and Best Practices

  • Managing session duration and timeouts
  • Organizing code efficiently within notebooks
  • Strategies for long-running or production-grade tasks

Summary and Next Steps

Requirements

  • Prior experience in Python programming
  • Familiarity with Jupyter notebooks and foundational data analysis techniques
  • Understanding of standard machine learning workflows

Audience

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

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