Machine Learning

A Practical Guide to Deploying Machine Learning Models

Image by AuthorA Practical Guide to Deploying Machine Learning Models As a data scientist, you probably know how to build machine learning models. But it’s only when you deploy the model that you get a useful machine learning solution. And if you’re looking to learn more about deploying machine learning models, this guide is for you. The steps involved in building and deploying ML models can typically be summed up like so: building the model, creating an API to serve […]

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Python

Quiz: Single and Double Underscores in Python Names

Interactive Quiz ⋅ 9 QuestionsBy Leodanis Pozo Ramos Share In this quiz, you’ll test your understanding of Single and Double Underscores in Python Names. By working through this quiz, you’ll revisit Python naming conventions that rely on using underscores (_), how to differentiate public and non-public names by using a single leading underscore, how to use double leading underscores to leverage name mangling in Python classes, and other common uses of underscores in Python names. The quiz contains 9 questions […]

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Python

Should you use uv’s managed Python in production?

The uv Python packaging tool provides fast replacements for tools like pip, and a variety of developer experience improvements. Unlike most Python packaging tools, uv doesn’t require Python to be installed to use it. Building on that ease of installation, one of its interesting and useful features is the ability to install Python for you. As a developer, this is great: if you need a version of Python you don’t have installed, uv can install it for you (transparently, by […]

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Machine Learning

Interpreting and Communicating Data Science Results

As data scientists, we often invest significant time and effort in data preparation, model development, and optimization. However, the true value of our work emerges when we can effectively interpret our findings and convey them to stakeholders. This process involves not only understanding the technical aspects of our models but also translating complex analyses into clear, impactful narratives. This guide explores the following three key areas of the data science workflow: Understanding Model Output Conducting Hypothesis Tests Crafting Data Narratives […]

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Machine Learning

7 Scikit-Learn Secrets You Probably Didn’t Know About

Image by Author | Ideogram7 Scikit-Learn Secrets You Probably Didn’t Know About As data scientists with Python programming skills, we use Scikit-Learn a lot. It’s a machine learning package usually taught to new users initially and can be used right through to production. However, much of what is being taught is basic implementation, and Scikit-Learn contains many secrets to improve our data workflow. This article will discuss seven secrets from Scikit-Learn you probably didn’t know. Without further ado, let’s get […]

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Python

Quiz: Getting Started With Async Features in Python

Interactive Quiz ⋅ 7 QuestionsBy Martin Breuss Share In this quiz, you’ll test your understanding of Asynchronous Programming in Python. By working through this quiz, you’ll revisit the concepts of synchronous and asynchronous programs, why you might want to write an asynchronous program, and how to use Python async features. The quiz contains 7 questions and there is no time limit. You’ll get 1 point for each correct answer. At the end of the quiz, you’ll receive a total score. […]

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Python

Structural Pattern Matching in Python

Structural pattern matching is a powerful control flow construct invented decades ago that’s traditionally used by compiled languages, especially within the functional programming paradigm. Most mainstream programming languages have since adopted some form of pattern matching, which offers concise and readable syntax while promoting a declarative code style. Although Python was late to join the party, it introduced structural pattern matching in the 3.10 release. Getting to Know Structural Pattern Matching Before taking advantage of structural pattern matching in your […]

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Python

Using Type Hints for Multiple Return Types in Python

In Python, type hinting is an optional yet useful feature for making your code easier to read, reason about, and debug. With type hints, you let other developers know the expected data types for variables, function arguments, and return values. As you write code for applications that require greater flexibility, you may need to specify multiple return types to make your code more robust and adaptable to different situations. You’ll encounter different use cases where you may want to annotate […]

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Machine Learning

From Features to Performance: Crafting Robust Predictive Models

Feature engineering and model training form the core of transforming raw data into predictive power, bridging initial exploration and final insights. This guide explores techniques for identifying important variables, creating new features, and selecting appropriate algorithms. We’ll also cover essential preprocessing techniques such as handling missing data and encoding categorical variables. These approaches apply to various applications, from forecasting trends to classifying data. By honing these skills, you’ll enhance your data science projects and unlock valuable insights from your data. […]

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Machine Learning

Understanding RAG Part II: How Classic RAG Works

Understanding RAG Part I: How Classic RAG WorksImage by Editor | Midjourney & Canva In the first post in this series, we introduced retrieval augmented generation (RAG), explaining that it became necessary to expand the capabilities of conventional large language models (LLMs). We also briefly outlining what is the key idea underpinning RAG: retrieving contextually relevant information from external knowledge bases to ensure that LLMs produce accurate and up-to-date information, without suffering from hallucinations and without the need for constantly […]

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