Category: Machine Learning

This page contains short machine learning tutorials. However, very informative on the basics needs for someone learning the model, and tricks for someone more experienced. The lessons target machine learning with python, algorithms, pattern recognition, random forest classifier, regression, random forest explained, feature importance, etc.

Machine Learning

k-means-python

Painless Kmeans in Python – Step-by-Step with Sklearn

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8 de April de 2021

This tutorial shows how to use k-means clustering in Python using Scikit-Learn which can be installed using bioconda.

Machine Learning • Python

python r squared

Simple R Squared Calculation in Python

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12 de March de 2021

This short tutorial shows how to find the R squared value in Python using sklearn which can be useful when looking at the data correlation in …

Machine Learning

Painless Random Forest Regression in Python - Step-by-Step with Sklearn

Painless Random Forest Regression in Python – Step-by-Step with Sklearn

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5 de April de 2020

This tutorial demonstrates a step-by-step on how to use the Sklearn Python Random Forest package to create a regression model.

Machine Learning

Simple Near-duplicate String Detection

Simple Near-duplicate String Detection with LSH

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9 de February de 2020

This tutorial teaches you how to use Locality Sensitive Hashing (LSH) to detect near-duplicate sentences. Moreover, the task of identifying similar sentences is a common task …

Machine Learning

The 2 Most Important Use for Random Forest

The 2 Most Important Use for Random Forest

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17 de January de 2020

This tutorial demonstrates how to use the Sklearn Random Forest (a Python library package) to create a classifier and discover feature importance.

Bioinformatics • Data visualization • Machine Learning

Data Science Careers carving our professional paths

Data Science Careers carving our professional paths

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2 de January de 2020

Data science (or part of any of the data science careers) applies statistical procedures ranging from data transformations, data modeling, statistical and machine learning modeling, and …