Course Code: colabpro
Duration: 14 hours
Prerequisites:

  • Experience with Python programming
  • Familiarity with Jupyter notebooks and basic data analysis
  • An understanding of common machine learning workflows

Audience

  • Data scientists and analysts
  • Machine learning engineers
  • Python developers working on AI or research projects

Overview:

Google Colab Pro is a cloud-based environment for scalable Python development, offering high-performance GPUs, longer runtimes, and more memory for demanding AI and data science workloads.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Python users who wish to use Google Colab Pro for machine learning, data processing, and collaborative research in a powerful notebook interface.

By the end of this training, participants will be able to:

  • Set up and manage cloud-based Python notebooks using Colab Pro.
  • Access GPUs and TPUs for accelerated computation.
  • Streamline machine learning workflows using popular libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Integrate with Google Drive and external data sources for collaborative projects.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline:

Introduction to Google Colab Pro

  • Colab vs. Colab Pro: features and limitations
  • Creating and managing notebooks
  • Hardware accelerators and runtime settings

Python Programming in the Cloud

  • Code cells, markdown, and notebook structure
  • Package installation and environment setup
  • Saving and versioning notebooks in Google Drive

Data Processing and Visualization

  • Loading and analyzing data from files, Google Sheets, or APIs
  • Using Pandas, Matplotlib, and Seaborn
  • Streaming and visualizing large datasets

Machine Learning with Colab Pro

  • Using Scikit-learn and TensorFlow in Colab
  • Training models on GPU/TPU
  • Evaluating and tuning model performance

Working with Deep Learning Frameworks

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

Integration and Collaboration

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

Performance Optimization and Best Practices

  • Managing session lifetime and timeouts
  • Efficient code organization in notebooks
  • Tips for long-running or production-level tasks

Summary and Next Steps