No data science project is completed without data; I can even argue that you can't say "data science" without data. Often, in most data science projects, the data you need to analyze and use to build machine learning models are stored in a database somewhere. That somewhere sometimes is the web. You may collect data from a specific webpage about a certain product or from social media to uncover patterns or perform sentiment analysis. Regardless of why you are collecting the data or how you intend to use it, collecting data from the web -- web scraping -- is a task that can be quite tedious, but you will need to do for your project to achieve its goals.

Use of deep learning in computer vision has increased in the last decade. In the past couple of years, computer vision applications such as face detection and vehicle detection have become mainstream. One of the reasons is the availability of pre-trained models. Convinced by the success of deep learning on these applications, businesses have now started to solve their own problems using deep learning. But what if the available pre-trained models are not suitable for your application?

When I was in college, an ice cream shop opened nearby, and a few friends and I went to check it out. We walked in, and it looked completely normal -- they had all the usual flavors like mint, chocolate, and the like. However, at the end of the counter, they had this flavor called "The Broccoli Surprise". A naturally curious individual, I had to try it. I asked the attendant behind the counter for a sample.

Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there's a lot of data to analyse and to play with. This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we'll see different options to collect data from Twitter.

Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there's a lot of data to analyse and to play with. This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we'll see different options to collect data from Twitter.

Machine Learning systems are complex. At their core, they ingest data in a certain format, to build models that are able to predict the future. A famous example in the industry is identifying fragile customers, who may stop being customers within a certain number of days (the "churn" problem). These predictions only become valuable when they are used to inform or to automate decisions (e.g. which promotional offers to give to which customers, to make them stay). In many organizations, there is often a disconnect between the people who are able to build accurate predictive models, and those who know how to best serve the organization's objectives.

A major challenge in developing dialog systems is obtaining realistic data to train the systems for specific domains. We study the opportunity for using crowdsourcing methods to collect dialog datasets. Specifically, we introduce ChatCollect, a system that allows researchers to collect conversations focused around definable tasks from pairs of workers in the crowd. We demonstrate that varied and in-depth dialogs can be collected using this system, then discuss ongoing work on creating a crowd-powered system for parsing semantic frames. We then discuss research opportunities in using this approach to train and improve automated dialog systems in the future.