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Duration 14 hours
Course Outline
Introduction to Google Colab Pro
- Comparing Colab and Colab Pro: key features and constraints.
- Notebook creation and management strategies.
- Configuring hardware accelerators and runtime parameters.
Python Programming in Cloud Environments
- Structuring code cells, markdown, and notebook layouts.
- Installing packages and setting up development environments.
- Saving and version-controlling notebooks via Google Drive.
Data Processing and Visualization Techniques
- Ingesting and analyzing data from files, Google Sheets, or APIs.
- Leveraging Pandas, Matplotlib, and Seaborn for analysis.
- Streaming and visualizing large-scale datasets.
Machine Learning with Colab Pro
- Implementing Scikit-learn and TensorFlow within Colab.
- Training models on GPU/TPU infrastructure.
- Assessing and fine-tuning model performance.
Utilizing Deep Learning Frameworks
- Working with PyTorch in the Colab Pro environment.
- Optimizing memory and runtime resource usage.
- Saving checkpoints and recording training logs.
Integration and Collaboration Tools
- Mounting Google Drive and accessing shared datasets.
- Collaborating through shared notebook interfaces.
- Exporting work to GitHub or PDF for distribution.
Performance Optimization and Best Practices
- Managing session duration and timeout settings.
- Organizing code efficiently within notebooks.
- Best practices for long-running or production-grade tasks.
Summary and Next Steps
Requirements
- Practical experience in Python programming.
- Familiarity with Jupyter notebooks and foundational data analysis techniques.
- A solid grasp of standard machine learning workflows.
Target Audience
- Data scientists and analysts.
- Machine learning engineers.
- Python developers engaged in AI or research initiatives.
