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Programming for Everybody (Getting Started with Python)
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Explore techniques to optimize Python startup speed by embedding code objects into the cPython binary, reducing module loading time and improving performance for frequently launched applications.
Explore CPython's inner workings: from code compilation to execution. Learn about tokenization, parsing, bytecode, and the stack-based virtual machine, suitable for Python developers of all levels.
Explore tree-based ensemble models like Random Forest and Gradient Boosting, understanding their theory and implementation in Python for effective data science applications.
Explore data-driven humanitarian and healthcare projects using Python, with audience experiments and inspiration for running your own scientific experiments using the Python stack.
Explore Python-based online tools for analyzing air quality data, integrating libraries like Pandas and NumPy with Django and Plot.ly to create interactive visualizations and support research and education.
Explore Quart, an asyncio-based alternative to Flask, offering enhanced features like HTTP/2 and websockets while maintaining Flask's API for efficient web development.
Explore Pickle's security risks, potential defenses, and alternatives for safe Python object serialization in distributed systems.
Explore how AWS Lambda and Python concurrency can optimize a genetic algorithm for winning the SushiGo card game, discussing implementation, challenges, and results.
Explore GraphQL implementation in Python using Flask and SQLAlchemy, covering basics, advanced techniques, and tools for efficient API development and documentation.
Explore Domain-Driven Design patterns in Python, learning to model complex domains and implement tactical patterns for robust, maintainable software in large-scale projects.
Explore feature engineering and selection techniques to improve machine learning model accuracy. Learn how to develop an ensemble for measuring feature importance in real-world datasets.
Discover Python's power to automate office tasks, from creating Excel reports and PowerPoint presentations to manipulating PDFs, enhancing productivity and reducing manual work.
Explore the evolution of asynchronous programming in Python, from regular functions to async/await syntax. Learn about iterators, generators, and coroutines while discovering how to write efficient async code.
Streamline open-source project maintenance by automating testing and deployment using Github and Travis. Learn to create an efficient release pipeline, reducing errors and simplifying the process.
Explore common statistical fallacies and learn to recognize misleading data interpretations. Gain insights on correlation, bias, and significance to draw accurate conclusions from statistics.
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