Calculating the Uncertainty Coefficient (Theil’s U) in Python
A measure of correlation between discrete (categorical) variables
machine-learning
The m=√p rule for random forests | R-bloggers
A couple of days ago, in our lab session, we discussed random forrests, and, since it was based on the example in ISLR, we had a quick discussion about the random choice of features, and the “” rule Interestingly, on that one, we can play a bit, and try all choices, and do it…
The m=√p rule for random forests
A couple of days ago, in our lab session, we discussed random forrests, and, since it was based on the example in ISLR, we had a quick discussion about the random choice of features, and the “” rule Interestingly, on that one, we can play a bit, and try all choices, and do it…
10 GitHub Repositories for Advanced Machine Learning Projects
Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article.
Marketing Mix Modeling (MMM): How to Avoid Biased Channel Estimates
Learn which variables you should and should not take into account in your model.
Counts Outlier Detector: Interpretable Outlier Detection
An interpretable outlier detector based on multi-dimensional histograms.
Basis Functions: Simple Definition - Statistics How To
Types of Functions > Basis functions (called derived features in machine learning) are building blocks for creating more complex functions. In other
Cosine Similarity
Cosine similarity can measure the proximity between two documents by transforming words into vectors within a vector space.
7 Cool Technical GenAI & LLM Job Interview Questions
Cool LLM and GenAI tech questions covering many modern concepts, including fast vector search, contextual tokens, and augmented structures
Permutation Feature Importance from Scratch
Understanding the importance of permutations in the field of explainable AI
Tips for LLM Pretraining and Evaluating Reward Models
Discussing AI Research Papers in March 2024
SVM and Kernels: The Math that Makes Classification Magic
Imagine you're at a party separating people who love pizza (yum!) from those who...well, have...
A Benchmark and Taxonomy of Categorical Encoders
New. Comprehensive. Extendable.
Algorithm Repository
Customers Prefer to Crowdfund Products They Can Improve
Platforms like Kickstarter and Indiegogo have not only broadened access to funding to companies that might struggle in the capital markets but have also transformed the way companies connect with consumers during product development, replacing focus groups with real customers…
Speech and Language Processing
CatBoost - state-of-the-art open-source gradient boosting library with cate
#CatBoost - state-of-the-art open-source gradient boosting library with categorical features support,
What Is a Schur Decomposition? – Nick Higham
A Schur decomposition of a matrix $latex A\in\mathbb{C}^{n\times n}$ is a factorization $LATEX A = QTQ^*$, where $LATEX Q$ is unitary and $LATEX T$ is upper triangular. The diagonal entries of $LAT…
Encoding Categorical Variables: A Deep Dive into Target Encoding
Data comes in different shapes and forms. One of those shapes and forms is known as categorical data.
Getting started predicting time series data with Facebook Prophet
This article aims to take away the entry barriers to get started with time series analysis in a hands-on tutorial using Prophet
The Math behind Adam Optimizer
Why is Adam the most popular optimizer in Deep Learning? Let’s understand it by diving into its math, and recreating the algorithm.
3 Key Encoding Techniques for Machine Learning: A Beginner-Friendly Guide
How should we choose between label, one-hot, and target encoding?
Understanding Latent Dirichlet Allocation (LDA) — A Data Scientist’s Guide
LDA explained with a dog pedigree model
An Overview of Contextual Bandits
A dynamic approach to treatment personalization
Pearson vs Spearman Correlation: Find Harmony between the Variables
Which measure of correlation should you use for your task? Learn all you need to know about Pearson and Spearman correlations.
The Perfect Way to Smooth Your Noisy Data
Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.
10 Noteworthy AI Research Papers of 2023
This year has felt distinctly different. I've been working in, on, and with machine learning and AI for over a decade, yet I can't recall a time when these fields were as popular and rapidly evolving as they have been this year. To conclude an eventful 2023 in machine learning…
Boosting Algorithms in Machine Learning, Part I: AdaBoost
Understanding the logic behind AdaBoost and implementing it using Python
An unusual introduction to manifolds
This tutorial offers a bridge between the abstract mathematics of manifolds and computational practice.
Market Basket Analysis using Python
In this article, I'll take you through the task of Market Basket Analysis using Python. Market Basket Analysis using Python.
The Power of Independent Component Analysis (ICA) on Real-World Application
Independent component analysis (ICA) is a powerful data-driven tool capable of separating linear contributions in the data
Math for Machine Learning: 14 Must-Read Books
It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression…
A Gentle Introduction to Complementary Log-Log Regression
An alternative of logistic regression in special conditions
No sacred masterpieces
Or "that time I built Excel for Uber and they ditched it like a week after launch"
XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees
A world without if
Dirty Secrets of BookCorpus, a Key Dataset in Machine Learning
BookCorpus has helped train at least thirty influential language models (including Google’s BERT, OpenAI’s GPT, and Amazon’s Bort), according to HuggingFace. This is the research question that…
Pearson, Spearman and Kendall Correlation Coefficients, by Hand
Learn how to compute the Pearson, Spearman and Kendall correlation coefficients by hand to evaluate the relationship between two variables
Machine Learning Using Decision Trees in Ruby
In the era of hyper-sophisticated machine learning models like ChatGPT, it is surprising how effective the classic decision tree model remains, especially when used in conjunction with other techniques, such as bagging, boosting and random forests. In this blog post we…
Probabilistic Machine Learning: Advanced Topics
Dynamic Pricing with Multi-Armed Bandit: Learning by Doing!
Applying Reinforcement Learning strategies to real-world use cases, especially in dynamic pricing, can reveal many surprises
Kernel Density Estimation explained step by step
Intuitive derivation of the KDE formula
Why is Feature Scaling Important in Machine Learning? Discussing 6 Feature
Standardization, Normalization, Robust Scaling, Mean Normalization, Maximum Absolute Scaling and Vector Unit Length Scaling
Evaluation Metrics for Recommendation Systems — An Overview
Understanding the purpose and functionality of common metrics in ML packages
patchy631/machine-learning
Self-Organizing Maps
Learn how Self-Organizing Maps work and why they are a useful unsupervised learning algorithm
Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning a
Discover the concepts of Zero-Shot, One-Shot, and Few-Shot Learning, which enable machine learning models to classify and recognize objects or patterns with a limited number of examples.
Machine Learning Basics: Polynomial Regression
Learn to build a Polynomial Regression model to predict the values for a non-linear dataset.
Geographic Clustering with HDBSCAN
How to explore geographic data with HDBSCAN, H3, graph theory, and OSM.
Mastering Monte Carlo: How To Simulate Your Way to Better Machine Learning
How a Scientist Playing Solitaire Forever Changed the Game of Statistics
LGBMClassifier: A Getting Started Guide
This tutorial explores the LightGBM library in Python to build a classification model using the LGBMClassifier class.