We’re going to look at seven tools and techniques that go beyond Pandas, things built for bigger data, faster execution, and more efficient pipelines.
Image Augmentation Techniques to Boost Your CV Model Performance
How flipping, rotating, zooming, and adjusting images’ visual properties can help boost computer vision model performance.
10 Critical Mistakes that Silently Ruin Machine Learning Projects
A tour across the lifecycle of a machine learning system development to highlight and describe 10 critical (and sometimes subtle) mistakes that could derail a machine learning project.
Zero-Shot and Few-Shot Classification with Scikit-LLM
A look inside the zero-shot and few-shot classification capabilities of Scikit-LLM and how to use them alongside Scikit-learn workflows.
Building a Plain Seq2Seq Model for Language Translation
Sequence-to-sequence (seq2seq) models are powerful architectures for tasks that transform one sequence into another, such as machine translation. These models employ an encoder-decoder architecture, where the encoder processes the input sequence and the decoder generates an output sequence based on the encoder’s output. The attention mechanism was developed for seq2seq models, and understanding how seq2seq […]
Synthetic Dataset Generation with Faker
Introducing a versatile and powerful Python library for generating very realistic datasets, even with real-world-like imperfections.
From Linear Regression to XGBoost: A Side-by-Side Performance Comparison
Two types of machine learning models for regression. One popular dataset to be fitted. Which one wins?
Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models
This article briefly describes what LLM embeddings are and shows how to use them as engineered features for Scikit-learn models.
Revisiting k-Means: 3 Approaches to Make It Work Better
This tutorial will explore three of the most effective techniques to make k-means work better in the wild, specifically using k-means++ for smarter centroid initialization, leveraging the silhouette score to find the optimal number of clusters, and applying the kernel trick to handle non-spherical data.
Discussing Decision Trees: What Makes a Good Split?
This article takes a closer look at the inner workings of decision trees, focusing on how branches are created through deliberate, data-driven splitting (spoiler: it certainly doesn’t happen at random).