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Supervised Learning for Document Classification with Scikit-Learn - QuantStart. Document Classification with scikit-learn. Document Classification with scikit-learn. Document classification is a fundamental machine learning task.

Document Classification with scikit-learn

It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we're going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we'll have by the end of this tutorial is effective, and surprisingly accurate. Spam filtering is kind of like the "Hello world" of document classification.

It's a binary classification problem: either spam, or not spam (a.k.a ham). We're going to use a combination of the Enron-Spam (in raw form) data sets and the SpamAssassin public corpus. Loading raw email data into a workable formatExtracting features from the raw data that an algorithm can learn fromTraining a classifierEvaluating accuracy by cross-validationImproving upon initial accuracy.

Sentiment Analysis with Python and scikit-learn – Marco Bonzanini. Sentiment Analysis is a field of study which analyses people’s opinions towards entities like products, typically expressed in written forms like on-line reviews.

Sentiment Analysis with Python and scikit-learn – Marco Bonzanini

In recent years, it’s been a hot topic in both academia and industry, also thanks to the massive popularity of social media which provide a constant source of textual data full of opinions to analyse. This article discusses one particular application of sentiment analysis: sentiment classification at the document level. In other words, given a document (e.g. a review), the task consists in finding out whether it provides a positive or a negative sentiment towards the product being discussed.

The following paragraphs describe the setup and the main components or our classification example with samples of code in Python using scikit-learn, a popular machine learning library. The complete code is discussed at the end of this post, and available as Gist on Github. Setting up for the experiments. GitHub - pandastrike/bayzee: Text classification using Naive Bayes and Elasticsearch. Install predictionio on Mac OSX – Mac App Store.

About the App App name: predictionioApp description: Source machine learning serverApp website: Install the App.

Install predictionio on Mac OSX – Mac App Store

Pio-template-fpm/ at master · goliasz/pio-template-fpm. Quick Start - Complementary Purchase Engine Template. OpenNLP-SentimentAnalysis-Template/ at master · vshwnth2/OpenNLP-SentimentAnalysis-Template. Quick Start - Product Ranking Engine Template. Installing DeepDetect. DeepDetect is written in C++11 with professional support for Linux platforms.

Installing DeepDetect

The latest release can be found at DeepDetect Releases. Important: at the moment the only platform with support is Ubuntu 14.04 LTS. DeepDetect may also be compiled from source on other platforms such as OSX. If you need to do so and are experiencing difficulties, request help on Github. Docker and Amazon AMI images are available. Docker images Pre-built docker images for both CPU and GPU machines are available from Docker images are the way to get started very quickly:

Getting Started - Docker. Estimated reading time: 17 minutes Welcome to Docker Desktop!

Getting Started - Docker

The Docker Desktop for Mac section contains information about the Docker Desktop Community Stable release. For information about features available in Edge releases, see the Edge release notes. For information about Docker Desktop Enterprise (DDE) releases, see Docker Desktop Enterprise. Docker is a full development platform to build, run, and share containerized applications.

See Install Docker Desktop for download information, system requirements, and installation instructions. Check versions🔗 GitHub - dbpedia/lookup: Outputs a list of ranked DBpedia resources for a search string. SPARQL vs. SQL - Intro. This lesson compares the SPARQL and SQL query languages, which are designed to query RDF and relational data, respectively.

SPARQL vs. SQL - Intro

You may be reading this lesson because you know one and want to learn the other or because you need to make some decisions about which to use for some purpose. First, let's look at their data models; that is, the way we consider their data to be structured. Comparing RDF and SQL data Many people ask what can be done with SPARQL that can't be done with SQL, when in fact they care about what can be done in RDF that can't be done with relational databases. Both of these languages give the user access to create, combine, and consume structured data. Relational data is made up of rows of data collected into tables (also called a "relations" in formal relational literature). CREATE TABLE Person ( ID INT, fname CHAR(10), addr INT, FOREIGN KEY(addr) REFERENCES Address(ID) ); CREATE TABLE Address ( ID INT, city CHAR(10), state CHAR(2) ) <PersonA> a <Person> .

SELECT ? SELECT ? How to send location from Facebook messenger platform? Messenger-bot/echo-express.js at master · remixz/messenger-bot. Messenger-bot/ at master · remixz/messenger-bot. Node.JS Module Patterns. Node.JS Module Patterns Using Simple Examples.

Node.JS Module Patterns

New-inline-keyboards Bot 2.0 · Issue #109 · yagop/node-telegram-bot-api. Node.js - Telegram bot api inlineKeyboard not worked. Using self-signed certificates. Upload your certificate using the certificate parameter in the setWebhook method.

Using self-signed certificates

The certificate supplied should be PEM encoded (ASCII BASE64), the pem file should only contain the public key (including BEGIN and END portions). When converting from a bundle format, please split the file to only include the public key. Generating a self-signed certificate pair (PEM): Openssl Windows binaries for Openssl are available online. Emoji unicode characters for use on the web. Emoji Unicode Tables The following tables show commonly-supported Emoji that map to standardized Unicode characters.

Emoji unicode characters for use on the web

The additional sections refer to symbols that have no mapping to Japanese mobile carriers in Unicode's EmojiSources.txt. Bot API. The Bot API is an HTTP-based interface created for developers keen on building bots for Telegram.To learn how to create and set up a bot, please consult our Introduction to Bots and Bot FAQ.


Recent changes May 25, 2016. Dynos and the Dyno Manager. Last updated 03 December 2018 All Heroku applications run in a collection of lightweight Linux containers called dynos. This article describes dyno conventions on the Heroku platform. For information about dyno pricing, see the Heroku pricing overview. Dyno configurations Every dyno belongs to one of the three following configurations: Web: Web dynos are dynos of the “web” process type that is defined in your Procfile. Once a web or worker dyno is started, the dyno formation of your app will change (the number of running dynos of each process type) - and subject to dyno lifecycle, Heroku will continue to maintain that dyno formation until you change it.

The Dyno Manager The dyno manager keeps dynos running automatically; so operating your app is generally hands-off and maintenance free. Dyno types Heroku provides a number of different dyno types each with a set of unique properties and performance characteristics. File Conversion API - Pricing. The Zamzar API was built with security in mind, all personal information and files are secure and protected. What is a conversion credit ? Every successful conversion costs at least 1 credit. Conversions that are more intensive to process can cost more - our formats page has all the details. Are there any bandwith limits ? Yes - The first 50MB of any input file is included in your conversion cost. Api - How do I download a file or photo that was sent to my Telegram bot?

Getting started with Telegram bots - unnikked. On 24 June Telegram released the new Bot platform. You can now create or use existing bots to enhance your Telegram experience. The new Bot platform is shipped with a fancy HTTP API mechanism, so building a custom Bot is a breeze. I was not able to wait more and after having played with my friends with the official bots I started to figure out how to use the APIs provided. Since the interaction is based purely on HTTP requests it was easy to me to get start easily using only my command line and some curl commands. BotFather The bot BotFather as the name suggests is the “father” of all bots (say again bot please), talking to this fancy program will let you create new children alongside an API token.

Very Basic PHP Telegram Bot w/Webhooks. Note: This uses PHP and is hosted on the webYou need to be able to save the script to a secure https URL ie you need a valid SSL certificate.You need an authorization token. Follow the instructions in Step 1 here.You will also need your botname @yourbotname Overview: We are creating a script which will speak with Telegram by receiving JSON post variables and sending GET variables (ie in the URL).You will tell Telegram where to find this script is IE which URL to send new message info to (“Set Webhooks)You will test the script by messaging your new bot.

Step 1a: The Script. Very Basic PHP Telegram Bot w/Webhooks. Telebot/ at master · yukuku/telebot. Telegraf/ at develop · telegraf/telegraf. Apiai-telegram-bot/telegrambot.js at master · api-ai/apiai-telegram-bot. Create AI Chat Bot for Facebook. Welcome back! - MessageBird. Using an authorization header with Fetch in React Native. Basic HTTP authentication in Node.js using the request module » Hay Kranen.

Here’s an easy way to use basic authentication while using the request library for Node.js. Unfortunately request doesn’t come with an easy convenience parameter you can use, so you need to provide it by yourself. The common way is to add it as an extra HTTP header. Nlptutorial/ at master · neubig/nlptutorial. Top 10 Machine Learning APIs: AT&T Speech, IBM Watson, Google Prediction. Introducing The Twilio Module For Node.js. This is the first in a series of tutorial blog posts covering the use of Twilio’s new helper library for node.js. TextMagic SMS API.