Category: Courses
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Expert: idioms for clean API and operator overloading
This deep-dive explores idiomatic Python API design and operator overloading. Learn how to use dunder methods, delegation, and context management to craft expressive, maintainable APIs. Includes modern best practices, code samples, and design principles inspired by frameworks like NumPy, SQLAlchemy, and Pydantic.
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Using matplotlib/plotly for infographic-style outputs
Infographic-style data visualization is reshaping how engineers communicate insights. This guide explores modern techniques using Matplotlib and Plotly to create polished, data-rich visuals that blend scientific accuracy with design precision. Learn when to use each tool, best practices for layout and color, and how to integrate both in automated workflows.
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Best practices for feature importance ranking
Feature importance ranking is central to explainable machine learning. This guide explores modern post-2024 best practices, including model-based, permutation, and SHAP methods, with code examples and interpretability tips. Learn how leading teams integrate explainability into CI/CD workflows for reliable, transparent, and ethical AI.
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Introduction to lakehouse architecture
The lakehouse architecture bridges the gap between data lakes and warehouses, providing unified governance, performance, and flexibility. This post introduces the core principles, benefits, and technologies behind lakehouses, explaining why theyâve become the foundation for modern data platforms in 2025.
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Introduction to fairness in machine learning
Fairness in machine learning ensures that AI systems make equitable decisions across different groups. This beginner-friendly introduction covers key fairness definitions, sources of bias, core metrics, mitigation strategies, and the leading tools engineers use to build fair models.
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Empirical: LSTM vs Prophet vs ARIMA
This empirical deep dive compares LSTM, Prophet, and ARIMAâthree dominant paradigms in time series forecastingâacross accuracy, interpretability, and computational trade-offs. Drawing from post-2024 experiments, the post explores when each model shines, how they scale in production, and emerging hybrid trends for data scientists.
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Tools: abstract base classes and dataclasses for GRASP
Abstract Base Classes (ABCs) and dataclasses provide structural and conceptual clarity in Python applications. This post explores how these tools reinforce GRASP design principlesâlike low coupling, polymorphism, and information expertâoffering engineers practical patterns for building clean, maintainable, and scalable systems.
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Intro to scaling ML inference
Scaling machine learning inference efficiently is as critical as training a good model. As models grow larger and more complex, the challenge shifts from accuracy to throughput, latency, and cost optimization. This post introduces practical strategies, architectures, and tools used in 2025 to scale ML inference across CPUs, GPUs, and distributed environments.
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Best practices: reactive and event-driven observer systems
Learn how to design robust, event-driven observer systems using reactive principles. This guide covers best practices for architecture, error handling, observability, and performance optimization, with examples in Python, JavaScript, and modern reactive frameworks.