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The Emergence of the Modern Middle East - Part I
Six Sigma Part 1: Define and Measure
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Beginner-friendly introduction to machine learning using Python and open-source libraries. Learn to implement basic ML concepts without advanced math skills.
Discover an experimental Python profiler with low overhead, line-level insights, and no source code modification. Learn to identify quick optimizations for significant performance gains.
Explores antipatterns in Python library design, discussing type checking, API design, resource management, and networking. Emphasizes balancing convenience with flexibility, readability, and predictability.
Explore challenges and solutions in evolving microservices architecture, focusing on functional, cultural, and reliability aspects. Learn from real-world experiences and Python-specific tools for graceful growth.
Eight rules for simplifying development, focusing on clarity and manageable complexity. Learn to write code that's easier to understand, debug, and maintain.
Learn to build a scalable RESTful real-time analytics system using Pyramid, exploring architecture, tools, and deployment strategies for handling billions of page impressions in a Big Data context.
Explore modern authentication techniques, including UAF and U2F standards, to improve security and user experience. Learn integration methods for Python frameworks like Django.
Explore the architecture of a cloud hosting service using Django, Ansible, and Celery. Learn about VM management, microservices integration, and University of Cambridge IT services.
Learn to build a spreadsheet from scratch using Python, covering data representation, cell objects, formula evaluation, dependency graphs, and custom functions. Gain insights into spreadsheet mechanics.
Learn to design a scalable, distributed application using consul and uWSGI. Covers automatic service registration, health checking, distributed storage, and multi-datacenter awareness for global scalability.
Explore advanced testing techniques using Hypothesis for property-based testing and American fuzzy lop for fuzzing, enhancing code coverage and uncovering hidden bugs in Python applications.
Explore randomized data generation for robust unit testing using the Hypothesis library in Python, enhancing test coverage and detecting potential bugs through dynamic testing approaches.
Explore CPython object internals, from simple to complex types. Learn to manipulate instance data using ctypes and perform unconventional operations on Python objects.
Explore integrating machine learning with Plone 5 CMS using sklearn for content classification and clustering, featuring real-world use cases and enhanced frontend development.
Explore Python and PyPy performance optimization techniques, JIT compilation, and tools for analyzing program efficiency with expert PyPy developers Antonio Cuni and Maciej Fijalkowski.
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