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 Duration 14 hours

Course Outline

Getting Started with Google Colab Pro

  • Comparative analysis of Colab vs. Colab Pro: key features and constraints
  • Techniques for creating and organizing notebooks
  • Configuring hardware accelerators and runtime parameters

Cloud-Based Python Development

  • Structuring notebooks using code cells and markdown
  • Installing packages and configuring the development environment
  • Version control and storage of notebooks in Google Drive

Data Manipulation and Visualization

  • Ingesting and analyzing data from files, Google Sheets, or API endpoints
  • Utilizing Pandas, Matplotlib, and Seaborn for analysis
  • Handling and visualizing large-scale datasets

Implementing Machine Learning with Colab Pro

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

Utilizing Deep Learning Frameworks

  • Implementing PyTorch projects with Colab Pro
  • Managing memory usage and runtime resource allocation
  • Saving model checkpoints and training logs

Integration and Team Collaboration

  • Mounting Google Drive and accessing shared datasets
  • Facilitating teamwork through shared notebooks
  • Exporting content to GitHub or PDF for wider distribution

Performance Tuning and Best Practices

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

Conclusion and Future Directions

Requirements

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

Target Audience

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

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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