Category: Machine Learning

Articles About Machine Learning

Machine Learning

5 Free Books on Computer Vision

Image by Author | Ideogram5 Free Books on Computer Vision Computer vision is a branch of Artificial Intelligence (AI) that studies how machines can interpret and understand visual information, such as images and videos. Most computer vision models today are based on deep learning architectures like Convolutional Neural Networks (CNNs), which excel at tasks such as image classification, object detection, and segmentation. However, the necessary fundamentals to deeply understand the field date back to earlier times. To help you master […]

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Machine Learning

Understanding RAG III: Fusion Retrieval and Reranking

Understanding RAG III: Fusion Retrieval and RerankingImage by Editor | Midjourney & Canva Check out the previous articles in this series: Having previously introduced what is RAG, why it matters in the context of Large Language Models (LLMs), and what does a classic retriever-generator system for RAG look like, the third post in the “Understanding RAG” series examines an upgraded approach to building RAG systems: fusion retrieval. Before deep diving, it is worth briefly revisiting the basic RAG scheme we […]

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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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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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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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Machine Learning

A Roadmap for Your Machine Learning Career

A Roadmap for Your Machine Learning CareerImage by Author | Created on Canva Are you looking to make a career in machine learning? If so, this guide is for you. Machine learning is an interesting field with a lot of potential to solve real-world problems. However, going from a novice to a professional requires a structured approach that not only focuses on technical skills but also on understanding real-world applications, software engineering practices, and industry expectations. And this guide will […]

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Machine Learning

Planning Your Data Science Project

Effective data science projects begin with a strong foundation. This guide will walk you through the essential initial stages: understanding your data, defining project goals, conducting initial analysis, and selecting appropriate models. By carefully applying these steps, you will increase your chances of producing actionable insights. Let’s get started. Planning Your Data Science ProjectPhoto by Sven Mieke. Some rights reserved.   Understanding Your Data The foundation of any data science project is a thorough understanding of your dataset. Think of […]

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Machine Learning

Understanding RAG Part I: Why It’s Needed

Understanding RAG Part I: Why It’s NeededImage by Editor | Midjourney Natural language processing (NLP) is an area of artificial intelligence (AI) aimed at teaching computers to understand written and verbal human language and interact with humans by using such a language. Whilst traditional NLP methods have been studied for decades, the recent emergence of large language models (LLMs) has virtually taken over all developments in the field. By combining sophisticated deep learning architectures with the self-attention mechanism capable of […]

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