Machine Learning Pills | David Andrés | Substack

RW #7 - When to use Rules, ML or LLMs?

Deciding between a simple, rule-based system and a sophisticated machine learning (ML) model is a critical choice in software development. While it's…

Sep 20 • David Andrés

Issue #107 - Gaussian Mixture Models

Today we will introduce Gaussian Mixture Models (GMMs)! While K-Means is a fantastic starting point for clustering, it makes a rigid assumption: that…

Sep 14 • David Andrés

DIY #16 - Build a Persistent Conversational Agent with LangGraph

In this article, we'll build exactly that: a Python-based conversational agent using LangGraph that can remember user information across sessions and…

Sep 7 • David Andrés

Issue #106 - Introduction to K-means clustering

Today we will introduce K-means Clustering! K-means is one of the most popular and fundamental unsupervised learning algorithms in machine learning…

Aug 31 • David Andrés

Issue #105 - Randomisation & Stratification in A/B Testing

Now it’s time to tackle one of the most overlooked—but most critical—steps in the process: how to assign users into groups.A fair comparison depends on…

Aug 23 • David Andrés

DIY

DIY #16 - Build a Persistent Conversational Agent with LangGraph

In this article, we'll build exactly that: a Python-based conversational agent using LangGraph that can remember user information across sessions and…

Sep 7 • David Andrés

DIY #15 - Prompt Chaining with LangChain

Imagine you have a complex task, like writing a detailed report or analyzing a lengthy customer feedback document. Trying to get an AI to do it all in…

Jun 29 • David Andrés

DIY #14 - Step-by-step implementation of a ReAct Agent in LangGraph

Large Language Model (LLM) agents can make decisions about when to use external tools as part of answering a question. Let’s now cover the most basic…

Apr 20 • David Andrés

DIY#13 - Sentiment Analysis with Bag-of-Words

Today we’re diving into sentiment analysis—a cool way to teach our models to decide if a movie review is cheering us on or giving us the boot. We’ll…

Mar 8 • Muhammad Anas and David Andrés

DIY #12 - SHAP in Action: Making ML Explainable

Have you ever trained a machine learning model and thought, “Okay, it works… but why does it work?” Or maybe you’ve been asked, “Why did the model make…

Feb 15 • David Andrés and Muhammad Anas

Real-World

RW #7 - When to use Rules, ML or LLMs?

Deciding between a simple, rule-based system and a sophisticated machine learning (ML) model is a critical choice in software development. While it's…

Sep 20 • David Andrés

RW #6 - Text-Moderation System with Embeddings

This week we’re handing you a plug-and-play, notebook-ready tutorial you can drop straight into Jupyter or VS Code. Inside you’ll find:Why an…

Aug 3 • David Andrés

RW #5 - No-Code Customer Service agent with LangFlow

Imagine a customer service operation where AI agents don't just rely on generic responses, but actually understand the business inside and out. Agents…

Jul 13 • David Andrés

RW #4 - EDA applied to Netflix (part II)

Welcome back! In Part I, we laid the groundwork for exploring Netflix’s data—distributions, ratings, and top producing countries. Now, in Part II, we’re…

Apr 13 • Muhammad Anas and David Andrés

RW #3 - EDA applied to Netflix (part I)

Exploratory Data Analysis (EDA) is the foundation of any data-driven project. It’s where you get your first "feel" of the data — what hides beneath…

Mar 30 • David Andrés and Muhammad Anas