Dot Product in Linear Algebra for Data Science using Python
Building up the intuition for how matrices help to solve a system of linear equations and thus regressions problems
machine-learning
Isolation Forest from Scratch
Implementation of Isolation forest from scratch for further understanding of the algorithm
The Power-Law Distribution
Recursive Feature Elimination (RFE) for Feature Selection in Python
Recursive Feature Elimination, or RFE for short, is a popular feature selection algorithm. RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting…
A Simplified approach using PyCaret for Anomaly Detection
Explaining outlier detection with PyCaret library in python
What is isotonic regression?
Isotonic regression is a method for obtaining a monotonic fit for 1-dimensional data. Let’s say we have data such that . (We assume no ties among the ‘s for simplicity.) Informally, isotonic regression looks for such that the ‘s approximate … Continue reading →
Text Mining with R: Gathering and Cleaning Data
Case study of tweets from comments on Indonesia’s biggest media
Entropy and Information Gain
Yet another tool used to make Decision Tree splits.
Deploy Machine Learning Applications using Chef
Overview of deploying a model with the Chef — A Configuration Management Tool
How to Use Polynomial Feature Transforms for Machine Learning
Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified and modeled by a learning algorithm. Another approach is to engineer new features that expose these interactions and see if they improve…
Speeding training of decision trees
New method reduces training time by up to 99%, with no loss in accuracy.
Hierarchical Clustering: An Application to World Currencies
Do Asian currencies move in tandem? What about emerging markets in general? Are commodity currencies like AUD and CAD closely related as…
The Illustrated Guide To Classification Metrics: The Basics
An overview of the fundamentals behind measuring and comparing machine learning solutions
What, Why and How of t-SNE
Dimensionality Reduction using t-SNE in a nutshell
Categorical Feature Encoding in Python
Methods to encode categorical variables using Python
Understanding Associative Embedding
An elegant method to group predictions without labeling
Amazon’s AI tool can plan collision-free paths for 1,000 warehouse robots
Amazon researchers describe a machine learning system that plans the movements and paths of up to 1,000 mobile warehouse robots.
firmai/datagene: DataGene - Identify How Similar Datasets Are to One Anothe
DataGene - Identify How Similar TS Datasets Are to One Another (by @firmai) - firmai/datagene
Time Series Analysis: Creating Synthetic Datasets
How to create time series datasets with different patterns
Complete guide to machine learning and deep learning in retail
The stores aren’t dead yet
A picture is worth 1,000 false-positive bug reports
How and why we built a custom app for visual debugging of warehouse pick paths.
Open sourcing the AI Model Efficiency Toolkit
Qualcomm open sources the AI Model Efficiency Toolkit on GitHub, providing a simple library plugin for AI developers.
Latent Semantic Analysis: intuition, math, implementation
TL;DR — Text data suffers heavily from high-dimensionality. Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction…
Spatial Autocorrelation: Close Objects Affecting Other Close Objects
Deep dive into spatial autocorrelation and their industry use cases
An Intro to Graph Theory, Centrality Measurements, and NetworkX
Graph Theory is the study of graphs which are mathematical structures used to model pairwise relations between objects. These graphs are…
mlmachine - Clean ML Experiments, Elegant EDA & Pandas Pipelines
This new Python package accelerates notebook-based machine learning experimentation
An Intuitive Explanation of Kernels in Support Vector Machine (SVM)
We will walk through a simple example with basic arithmetics to demystify the concept of kernel.
Generative vs Discriminative Probabilistic Graphical Models
A Comparison of Naive Bayes and Logistic Regression
Using Q-Learning in Numpy to teach an agent to play a game
Using q-learning for sequential decision making and therefore learning to play a simple game.
7 advanced tricks in pandas for data science
Pandas is the go-to library for data science. These are the shortcuts I use to do repetitive data science tasks faster and simpler.
5 Great New Features in Latest Scikit-learn Release - KDnuggets
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.
Modeling in Seconds: Using PyCaret as a Tool for Data Science Fast Decision
I came across Pycaret while I was browsing on a slack for data scientists. It's a versatile library in which you can apply/evaluate/tune…
Cross Entropy, Log-Loss And Intuition Behind It
In this blog, you will get an intuition behind the use of cross-entropy and log-loss in machine learning.
How to Deploy your Machine Learning Models on Kubernetes
Deploy, scale and manage your machine learning services with Kubernetes and Terraform on GCP.
SVM and Kernel SVM
Learn about SVM or Support Vector Machine, Kernel Trick, Hyperplanes, Lagrange Multipliers using visual examples and code sections.
PyTorch BentoML Heroku: The simple stack
and how to train and deploy an ML model into production with them.
Handling imbalanced dataset in supervised learning using family of SMOTE algorithm. - DataScienceCentral.com
Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t last long when you look at the confusion matrix and realize that majority class is 98% of the total data and all…
Feature Engineering: Data scientist's Secret Sauce ! - DataScienceCentral.com
originally posted by the author on Linkedin : Link It is very tempting for data science practitioners to opt for the best known algorithms for a given problem.However It’s not the algorithm alone , which can provide the best solution ; Model built on carefully engineered and…
L1 and L2 Regularization — Explained
How to control the complexity of a model
Netflix Data Science Interview Practice Problems
A walkthrough of some of Netflix’s interview questions!
Detecting Weird Data: Conformal Anomaly Detection
Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of…
30 Data Science Interview Questions from FAANG Tech Giants
In-depth Interview Q&A from Facebook, Amazon, Apple, Netflix, and Google
Machine Learning in Industrial Chemicals: Process Quality Optimization
This post is the last in our series of 5 blog posts highlighting use case presentations from the 2nd Edition of Seville Machine Learning School (MLSEV). You may also check out the previous posts ab…
A brief introduction to the beauty of Information Theory
Lambdaclass's blog about distributed systems, machine learning, compilers, operating systems, security and cryptography.
Lines Detection with Hough Transform
An algorithm to find lines in images
A Deep Dive into Lane Detection with Hough Transform
A detailed step-by-step guide to build a Lane Line Detection algorithm in OpenCV.
Getting Started with Spectral Clustering - Dr. Juan Camilo Orduz
Stochastic-, Batch-, and Mini-Batch Gradient Descent Demystified
Why do we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks?
Gaussian Mixture Models(GMM)
Understanding GMM: Idea, Maths, EM algorithm & python implementation
Stacked Auto-encoder as a Recommendation System for Movie Rating Prediction
Introduction on Stacked Auto-encoder and Technical Walk-through on Model Creation using Pytorch