Since the beginning of the brief history of Natural Language Processing (NLP), there has been the need to transform text into something a machine can understand. That is, transforming text into a meaningful vector (or array) of numbers. The de-facto standard way of doing this in the pre-deep learning era was to use a […]
As a business development person in a Machine Learning company, I get confronted every day with the changes happening in the role of sales.
As organizations evolve to become ready for the disruption of automation, there’s a growing need to understand what these technologies can and cannot do.
In this blog post, we’ll take a look […]
Have you ever wanted to get your company featured in TIME Magazine, Reuters or Mashable? It could happen through Help a Reporter Out (HARO) and get this media coverage for free by answering HARO requests.
But why spend time going through every HARO query when it can be done automatically with machine learning? This is […]
Today we’re launching Inbox Samples, an exciting new feature that will make it much easier to improve the machine learning models built on our platform.
Now, whenever you send a new text to be analyzed by MonkeyLearn (via our API, integrations or user interface), the system will save your data within the Inbox of your […]
Machine learning is eating the world right now. Everyone and their mother are learning about machine learning models, classification, neural networks, and Andrew Ng. You’ve decided you want to be a part of it, but where to start?
In this article we’ll cover some important characteristics of Python and why it’s great for machine learning. […]
We are excited to announce our MonkeyLearn integration with Zapier!
Wouldn’t be amazing if you had a simple idea on how to automate a manual workflow with AI and just try it out in a couple of minutes?
Labeling your emails, tagging customer support tickets or organizing billing invoices are just a few examples of manual […]
So you’re working on a text classification problem. You’re refining your training set, and maybe you’ve even tried stuff out using Naive Bayes. But now you’re feeling confident in your dataset, and want to take it one step further. Enter Support Vector Machines (SVM): a fast and dependable classification algorithm that performs very well with a limited amount of data. […]
The simplest solutions are usually the most powerful ones, and Naive Bayes is a good proof of that. In spite of the great advances of the Machine Learning in the last years, it has proven to not only be simple but also fast, accurate and reliable. It has been successfully used for many purposes, but it works particularly well with natural language processing (NLP) problems. […]
This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry. […]
This is the second part in a series where we analyze thousands of articles from tech news sites in order to get insights and trends about startups.
Last time around we scraped all the articles ever published in TechCrunch, VentureBeat and Recode using Scrapy. We then filtered out all the articles that weren’t about startups, […]