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Course Outline

Introduction to Google Colab Pro

  • Comparative analysis: Colab vs. Colab Pro features and constraints
  • Notebook creation and management strategies
  • Overview of hardware accelerators and runtime configurations

Cloud-Based Python Programming

  • Structure of code cells, markdown, and notebooks
  • Installing packages and configuring development environments
  • Saving and managing notebook versions through Google Drive

Data Processing and Visualization

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

Machine Learning with Colab Pro

  • Implementing Scikit-learn and TensorFlow within the Colab environment
  • Training models leveraging GPU or TPU resources
  • Assessing and fine-tuning model performance

Utilizing Deep Learning Frameworks

  • Integrating PyTorch with Colab Pro
  • Optimizing memory usage and runtime resource allocation
  • Managing checkpoints and training logs

Integration and Team Collaboration

  • Mounting Google Drive and accessing shared datasets
  • Facilitating team collaboration through shared notebooks
  • Exporting results to GitHub or PDF for sharing

Performance Optimization and Best Practices

  • Managing session lifetimes and preventing timeouts
  • Organizing code effectively within notebooks
  • Strategies for executing long-running or production-grade tasks

Summary and Future Directions

Requirements

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

Target Audience

  • Data scientists and business analysts
  • Machine learning engineers
  • Python developers focused on AI or research-based projects
 14 Hours

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