Text Classification

Data Version Control Tutorial – dataversioncontrol


Today the data science community is still lacking good practices for organizing their projects and effectively collaborating. ML algorithms and methods are no longer simple "tribal knowledge" but are still difficult to implement, manage and reuse. To address the reproducibility we have build Data Version Control or DVC. This example shows you how to solve a text classification problem using the DVC tool. Git branches should beautifully reflect the non-linear structure common to the ML process, where each hypotheses can be presented as a Git branch. However, inability to store data in a repository and the discrepancy between code and data make it extremely difficult to manage a data science project with Git.

Regression vs. Classification Algorithms


Machine learning generates a lot of buzz because it's applicable across such a wide variety of use cases. That's because machine learning is actually a set of many different methods that are each uniquely suited to answering diverse questions about a business. To better understand machine learning algorithms, it's helpful to separate them into groups based on how they work.

New machine-assisted text classification on Content Moderator now in public preview


Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. In contrast to the existing text moderation service that flags profanity terms, the text classification feature helps detect potentially undesired content that may be deemed as inappropriate depending on context. In addition, to convey the likelihood of each category it may recommend a human review of the content. The text classification feature is in preview and supports the English language.

Multi-Class Text Classification with PySpark – Towards Data Science


Apache Spark is quickly gaining steam both in the headlines and real-world adoption, mainly because of its ability to process streaming data. With so much data being processed on a daily basis, it has become essential for us to be able to stream and analyze it in real time. In addition, Apache Spark is fast enough to perform exploratory queries without sampling. Many industry experts have provided all the reasons why you should use Spark for Machine Learning? So, here we are now, using Spark Machine Learning Library to solve a multi-class text classification problem, in particular, PySpark.

Health Research is Time-Consuming and Expensive, but Machine Learning Could Change That


From climate change to opioid addiction, we are facing serious public health crises that put our research and data management experts to the test. When it comes to scientific evidence, systematic literature reviews--painstaking assessments of all the literature ever produced on a given subject--are often regarded as the gold standard. Though no research method is foolproof, says Vox health correspondent Julia Belluz, "these studies represent the best available syntheses of global evidence about the likely effects of different decisions, therapies and policies." That comprehensiveness comes at high price, though, in terms of time and money. It involves sifting through enormous volumes of literature--sometimes hundreds of thousands of scientific abstracts--stored in academic databases.

Build your own object classification model in SageMaker and import it to DeepLens Amazon Web Services


We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using the command sudo apt-get install awscam on the Ubuntu terminal. For this tutorial, you need the MXNet version 0.12. You can update the MXNet version by using the command sudo pip3 install mxnet 0.12.1.

Text Classification: Applications and Use Cases


Text analysis, as a whole, is an emerging field of study. Fields such as Marketing, Product Management, Academia, and Governance are already leveraging the process of analyzing and extracting information from textual data. We discussed the technology behind Text Classification, one of the essential parts of Text Analysis. Text classification or Text Categorization is the activity of labeling natural language texts with relevant categories from a predefined set. In laymen terms, text classification is a process of extracting generic tags from unstructured text. These generic tags come from a set of pre-defined categories. Classifying your content and products into categories help users to easily search and navigate within website or application.

How Machine Learning, Classification Models Impact Marketing Ethics - InformationWeek


People seek convenience in their experiences with brands. Brands have begun to use machine learning classification to know who, where, and when to direct resources to provide that convenience.