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The article explains the algorithm behind the recently introduced Python package named PyHard,…

Accuracy, F1, or TPR (a.k.a recall or sensitivity) are well-known and widely used metrics for evaluating and comparing the performance of machine learning based classification.

But, are we sure we evaluate classifiers' performance correctly? Are they or others such as BACC (Balanced Accuracy),…

In a previous blog-post we have seen how we can use Signal Processing techniques for the classification of time-series and signals.

A very short summary of that post is: We can use the Fourier Transform to  transform a signal from its time-domain to its frequency domain. The peaks in the frequency spectrum indicate the most…

Dlib is an open source C++ framework containing various machine learning algorithms and many other complementary stuff which can be used for image processing, computer vision, linear algebra calculations and many other things. It has very good documentation and a lot of useful examples. In this post I will show how to use this library for solving a classification problem on Iris data…

Shark-ML is an open-source machine learning library which offers a wide range of machine learning algorithms together with nice documentation, tutorials and samples. In this post I will show how to use this library for solving classification problem, with two different algorithms SVM and Random Forest. This post will tell you about how to use API for:

1. Loading data

2. Performing normalization and dimension…

Hello, with this article I'm starting series of articles about full featured C++ Machine Learning frameworks . This articles covers how to use Shogun library for solving classification problem. Shogun is an open-source machine learning library that offers a wide range of machine learning algorithms. From my point of view it's not very popular among professionals, but it have a lot of fans among enthusiasts and…

Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. We summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural…

Text Analytics with Python -- A Practical Real-World Approach to Gaining Actionable Insights from your Data

Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" published by Apress\Springer, is a book packed with 385 pages of useful information based on techniques, algorithms,…

For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. It is ideal for beginners because it has a really simple interface, it is well documented with many examples and tutorials.

Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, feature extraction and engineering,  and pre-processing the data. The interface is consistent over all of these methods, so…

In this blog post, I will discuss feature engineering using the Tidyverse collection of libraries. Feature engineering is crucial for a variety of reasons, and it requires some care to produce any useful outcome. In this post, I will consider a dataset that contains description of crimes in San Francisco between…

(This post originally appeared on recurrentnull.wordpress.com, as first part in a series on sentiment analysis of movie reviews.)

Imagine I show you a book review, on amazon.com, say. Imagine I hide the number of stars, – all you get to see is the number of stars. And now I’m asking you, that review, is it good or bad?…

Text classification (a.k.a. text categorization) is one of the most prominent application of Machine Learning. The purpose of text classification is to give conceptual organization to large collection of documents.An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for…

One of the most popular methods or frameworks used by data scientists at the Rose Data Science Professional Practice Group is Random Forests. The…