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
- Comparing Colab and Colab Pro: Key features and constraints
- Notebook creation and management strategies
- Configuring hardware accelerators and runtime parameters
Cloud-Based Python Development
- Managing code cells, markdown, and notebook architecture
- Installing packages and configuring the environment
- Versioning and storing notebooks via Google Drive
Data Handling and Visualization
- Ingesting and analyzing data from files, Google Sheets, or APIs
- Leveraging Pandas, Matplotlib, and Seaborn
- Processing and visualizing large-scale datasets
Implementing Machine Learning with Colab Pro
- Applying Scikit-learn and TensorFlow within Colab
- Training models utilizing GPU or TPU resources
- Assessing and refining model performance
Utilizing Deep Learning Frameworks
- Integrating PyTorch with Colab Pro
- Overseeing memory usage and runtime resources
- Managing checkpoints and training logs
Integration and Team Collaboration
- Mounting Google Drive and accessing shared datasets
- Collaborating through shared notebook environments
- Exporting content to GitHub or PDF for distribution
Performance Tuning and Best Practices
- Controlling session duration and timeout settings
- Structuring code efficiently within notebooks
- Recommendations for long-duration or production-grade tasks
Conclusion and Further Development
Requirements
- Proficiency in Python programming.
- Working knowledge of Jupyter notebooks and fundamental data analysis techniques.
- Familiarity with standard machine learning processes.
Target Audience
- Data scientists and analysts.
- Machine learning engineers.
- Python developers focused on AI or research initiatives.