Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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