Get in Touch
 Duration 14 hours

Course Outline

Overview of Google Colab Pro

  • Distinguishing Colab from Colab Pro: features and constraints
  • Notebook creation and management processes
  • Hardware accelerators and runtime configuration settings

Cloud-Based Python Programming

  • Code cells, markdown formatting, and overall notebook architecture
  • Installing packages and configuring the development environment
  • Storing and version-controlling notebooks via Google Drive

Data Processing and Visualization Techniques

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

Machine Learning with Colab Pro

  • Implementing Scikit-learn and TensorFlow within the Colab environment
  • Model training utilizing GPU/TPU resources
  • Model performance evaluation and parameter tuning

Utilizing Deep Learning Frameworks

  • Integrating PyTorch with Colab Pro
  • Monitoring memory usage and managing runtime resources
  • Saving model checkpoints and training logs

Integration and Team Collaboration

  • Mounting Google Drive and accessing shared datasets
  • Collaborative work through shared notebooks
  • Exporting results to GitHub or PDF for easy distribution

Performance Optimization and Best Practices

  • Managing session duration and preventing timeouts
  • Structuring code efficiently within notebooks
  • Strategies for handling long-running or production-level tasks

Summary and Future Directions

Requirements

  • Proficiency in Python programming
  • Experience with Jupyter notebooks and fundamental data analysis techniques
  • A solid grasp of standard machine learning workflows

Target Audience

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

Number of participants


Price per participant

Upcoming Courses

Related Categories