GLMs Part I: A Rigorous Mathematical Formulation | by Andrew Rothman | Apr,

Intuition for Unifying Theory of GLMs with Derivations in Canonical and Non-Canonical Forms

GLMs Part II: Newton-Raphson, Fisher Scoring, & Iteratively Reweighted Leas

Generalized Linear Models (GLMs) play a critical role in fields including Statistics, Data Science, Machine Learning, and other computational sciences. In Part I of this Series, we provided a…

The 5 Feature Selection Algorithms every Data Scientist should know

Bonus: What makes a good footballer great?

Prophet | Forecasting at scale.

Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.

11 Dimensionality reduction techniques you should know in 2021

Reduce the size of your dataset while keeping as much of the variation as possible

Kolmogorov Complexity: Extensions and Applications

Principal Component Analysis explained visually

https://betterexplained.com/articles/hyperbolic-functions/

Amazon open-sources library for prediction over large output spaces

Framework improves efficiency, accuracy of applications that search for a handful of solutions in a huge space of candidates.

Notebook on nbviewer

Check out this Jupyter notebook!

Advanced forecasting using Bayesian diffusion modeling

Applications from cancer to covid-19

Nine Emerging Python Libraries You Should Add to Your Data Science Toolkit in 2022

As Data Science continues to grow and develop, it’s only natural for new tools to emerge, especially considering the fact that data…

Instacart Market Basket Analysis. Winner’s Interview: 2nd place, Kazuki… |

Winner’s Interview: 2nd place, Kazuki Onodera

Best Practices for Using AI to Develop the Most Accurate Retail Forecasting

A leading global retailer has invested heavily in becoming one of the most competitive technology companies around. Accurate and timely demand forecasting for millions of item-by-store combinations is…

Automate Hyperparameter Tuning for Multiple Models with Hyperopts

Automate your hyperparameter tuning with Sklearn Pipelines and Hyperopts for multiple models in a single python call. Let's dig into the process...

Deep In Singular Value Decomposition

There are often times when working in Data Science where we might come across a feature that is very difficult to interpret by a computer. This is often because the dimensions of the data are much…

nbterm: Jupyter Notebooks in the terminal

Jupyter notebooks are mostly known for their web-based user interface, such as JupyterLab or the Classic Notebook. They offer a great user…

Spotify Genre Classification Algorithm

Supervised Machine Learning — SVM, RANDOM FOREST, LOGISTIC REGRESSION

A Summary of Active Learning Frameworks

If you are dealing with a classification task, I recommend the modAL. As for the sequence labeling task, the AlpacaTag is the only choice for you. Active learning could decrease the number of labels…

Gentle introduction to 2D Hand Pose Estimation: Approach Explained

Detailed tutorial on where to find a dataset, how to preprocess data, what model architecture and loss to use, and, finally, how to…

What Really IS a Matrix Determinant?

The geometric intuition behind determinants could change how you think about them.

A Primer on the EM Algorithm

Christian Zuniga, PhD

When and how to use power transform in machine learning

Let’s see this powerful tool of data pre-processing

Time Series Forecasting with PyCaret Regression Module

PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.

Zero-Shot Learning: Can you classify an object without seeing it before?

Data Science, Machine Learning, AI & Analytics

Using Gaussian Mixture Models to Transform User-Item Embedding and Generate Better User Clusters

Improve clustering of user-item embedding by using GMM to generate new and tighter features

9 Distance Measures in Data Science

The advantages and pitfalls of common distance measures

How to create custom scikit-learn classification and regression models

Scikit learn is *the* go to package for standard machine learning models in Python. It not only provides most of the core algorithms that…

DIY XGBoost library in less than 200 lines of python

XGBoost explained as well as gradient boosting method and HP tuning by building your own gradient boosting library for decision trees.

11 Times Faster Hyperparameter Tuning with HalvingGridSearch

Successive halving completely crushes GridSearch and RandomSearch

CPU-based algorithm trains deep neural nets up to 15 times faster than top

Rice University computer scientists have demonstrated artificial intelligence (AI) software that runs on commodity processors and trains deep neural networks 15 times faster than platforms based on graphics ...

Deploying a basic Streamlit app to Heroku

This article demonstrates the deployment of a basic Streamlit app (that simulates the Central Limit Theorem) to Heroku.

Beginner’s Guide to XGBoost for Classification Problems

Utilize the hottest ML library for state-of-the-art performance in classification

Deploying a basic Streamlit app

This article demonstrates the deployment of a basic Streamlit app (that predicts the Iris’ species) to Streamlit Sharing.

You are underutilizing shap values — feature groups and correlations

Your model is a lens into your data, and shap its telescope

AI Planning using Constraint Satisfaction Problems

Using Constraint Satisfaction Problems to solve AI Planning Problems.

Principal component regression

In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model.

Quadratic Discriminant Analysis

A deep introduction to Quadratic Discriminant Analysis (QDA) with theory and Python implementation

UCI Machine Learning Repository

Discover datasets around the world!

Evaluating Search Algorithms

The three-step framework Shopify's Data Science & Engineering team built for evaluating new search algorithms.

130 Machine Learning Projects Solved and Explained

Machine Learning Projects solved and explained for free

Prediction Intervals for Gradient Boosting Regression

This example shows how quantile regression can be used to create prediction intervals. See Features in Histogram Gradient Boosting Trees for an example showcasing some other features of HistGradien...

Polynomial Regression in Python

Machine Learning from Scratch: Part 4

Three Model Compression Methods You Need To Know in 2021

Creative techniques to make complex models smaller

5 Things You Should Know About Covariance

When dealing with problems on statistics and machine learning, one of the most frequently encountered terms is covariance. While most of…

A Beginner’s Guide to Image Augmentations in Machine Learning

Data Augmentation is one of the most important yet underrated aspects of a machine learning system …

3 Key Pieces of Information About Logistic Regression Algorithm

It is a simple yet very efficient algorithm

4 Machine learning techniques for outlier detection in Python

Machine learning-based outlier detection

UPDATED: Using R and H2O to identify product anomalies during the manufactu

Note. This is an update to article: Using R and H2O to identify product anomalies during the manufacturing process.It has some updates but also code optimization from Yana Kane-Esrig( https://www.linkedin.com/in/ykaneesrig/ ), as sh...

Xgboost regression training on CPU and GPU in python

GPU vs CPU training speed comparison for xgboost