Tensorscience.com - Open source machine learning in Python

Introduction to Scikit-learn: classifying poisonous mushrooms and glass types

30 November 2018

This tutorial is an introduction to using Scikit-learn for machine learning in Python, focused on building a classifier to separate poisonous from edible mushrooms and to separate different types of glass. Scikit-learn is an excellent library for this purpose. Besides the fact that it does a lot of useful things for you right out of the box, saving you coding time, it’s also easy to implement your code, and it also comes with a plethora of examples that can be found around the web. Among other things, the scikit-learn library can be used for clustering, classification and regression, and extracting ...

Natural Language Processing (NLP) in Python with NLTK: An Introduction

30 November 2018

This tutorial is on natural language processing (NLP) in Python with the excellent NLTK package. Natural language processing (NLP) is the domain of artificial intelligence concerned with developing applications and services that have the ability to parse and understand natural (or human) languages. Some examples of this are speech recognition in devices like Alexa and Google Home, sentiment analysis of tweets on Twitter to gauge the mood of investors, etc. If you already are somewhat familiar with NLP, we recommend you read the following guide in which we further apply the techniques elaborated on in this tutorial. Sentiment analysis in Python ...

Person detection in video streams using Python, OpenCV and deep learning

29 November 2018

This tutorial is on detecting persons in videos using Python and deep learning. After following the steps and executing the Python code below, the output should be as follows, showing a video in which persons are tagged once recognized. Neural networks trained for object recognition allow one to identify persons in pictures. Hence, we can decompose videos or live streams into frames and analyze each frame by turning it into a matrix of pixel values. This tutorial is part of a larger section on person recognition that covers 3 topics. Detecting persons in videos (this page) Tracking persons in videos Detecting ...

Sentiment analysis tutorial in Python: classifying reviews on movies and products

28 November 2018

Sentiment analysis in conjunction with machine learning is frequently employed to gain insight into how positive or negative a target group feels about a particular entity, such as a movie, product line or political candidate. The key method to uncovering this is collecting samples of text from the target group (be it tweets, customer service inquiries, or, in this tutorial's case, product reviews). It is a key part of natural language processing. This tutorial will guide you through the step-by-step process of sentiment analysis using a random forest classifier that performs pretty well. We will use Dimitrios Kotzias's Sentiment ...

Optical Character Recognition (OCR) with Python and Tesseract 4: An introduction

25 November 2018

This tutorial is an introduction to optical character recognition (OCR) with Python and Tesseract 4. Tesseract is an excellent package that has been in development for decades, dating back to efforts in the 1970s by IBM, and most recently, by Google. At the time of writing (November 2018), a new version of Tesseract was just released - Tesseract 4 - that uses pre-trained models from deep learning on characters to recognize text. This version can not only recognize scanned characters with great accuracy but also handwritten text, and performs much better than Tesseract 3. OCR is especially relevant for scanned images that contain ...

Crawling and scraping websites with Python: finding expired domains and broken links using Scrapy

24 November 2018

This tutorial is on crawling and scraping the web with Python and Scrapy, focusing on finding expired domains and broken links. This is one of the many things you can do with crawlers and scrapers. In machine learning applications and research, crawling links and scraping content from websites is common, particularly for content analysis and community discovery algorithms. For instance, websites linking to one another are often related to one another in some way and tend to belong to the same community content-wise. Using the free and open source Scrapy package in Python, the code in this guide scrapes the content ...