What You'll Learn
- Understand and implement parallel computing concepts using Dask in Python
- Work with large datasets using Dask DataFrames for scalable data manipulation
- Perform advanced numerical computations using Dask Arrays and lazy evaluation
- Build and optimize machine learning workflows with Dask-ML and joblib integration
- Use Dask schedulers effectively for performance tuning and distributed computing
- Profile performance, handle memory spilling, and apply best practices with Dask
- Practice with real-world datasets like flight delays to build scalable ML models
Requirements
- A PC with Python and Jupyter Notebook installed, a basic understanding of Python and data handling is helpful but not required, and a willingness to learn step by step.
Who This Course is For
- Data analysts who want to scale their workflows and handle large datasets with ease.
- Python users looking to implement parallel computing and optimize performance.
- Machine learning practitioners seeking to train models on big data using Dask.
- Students pursuing careers in data science, big data, or engineering with Python.
- Data engineers and developers who need to process and transform data at scale.
Your Instructor
Start-Tech Trainings
Analytics and ML academy
4.3 Instructor Rating
8,979 Reviews
199,288 Students
39 Courses
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