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# Introduction
Working with Python means relying on many of its built-in functions, especially for data science tasks. Popular functions like len, max, range, etc., are common in a data scientist's toolkit and useful in various situations. However, many built-in functions remain unrecognized because they are perceived as…
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Products centered around large language models (LLMs), like ChatGPT or Gemini, have changed how developers work, making it easier to generate working code without a complete understanding of the underlying concepts. Tools like GitHub Copilot or Cursor show that AI can suggest effective code and boost developer productivity. However,…
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# Introduction
Stress testing is crucial for understanding how your application behaves under heavy load. For machine learning-powered APIs, it is especially important because model inference can be CPU-intensive. By simulating a large number of users, we can identify performance bottlenecks, determine the capacity of our system, and ensure reliability.…
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# Introduction
Feature engineering gets called the 'art' of data science for good reason — experienced data scientists develop this intuition for spotting meaningful features, but that knowledge is tough to share across teams. You'll often see junior data scientists spending hours brainstorming potential features, while senior folks end…
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# Introduction
Picture this: you're working on a Python project, and every time you want to run tests, you type python3 -m pytest tests/ --verbose --cov=src. When you want to format your code, it's black . && isort .. For linting, you run flake8 src tests. Before you…
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# Introduction
Machine learning has become an integral part of many companies, and businesses that don't utilize it risk being left behind. Given how critical models are in providing a competitive advantage, it's natural that many companies want to integrate them into their systems.
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Ever run a Python script and immediately wished you hadn’t pressed Enter?
Debugging in data science is not just an act; it’s a survival skill — particularly when dealing with messy datasets or devising prediction models on which actual people rely.
In this article, we will explore the basics…
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In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Transforming this data into meaningful, structured inputs that models can learn from is an essential step — this process is known as feature engineering. Feature engineering can impact model performance, sometimes even more than the…
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Introduction
Python's built-in datetime module can easily be considered the go-to library for handling date and time formatting and manipulation in the ecosystem. Most Python coders are familiar with creating datetime objects, formatting them into strings, and performing basic arithmetic. However, this powerful module, sometimes alongside related libraries…