Breakthroughs in the field of Natural Language Processing (NLP) have seen a sudden rise in recent times. The amount of text data available to us is enormous, and data scientists are coming up with new and innovative solutions to parse through it and analyse patterns. From writing entire novels to decoding ancient texts, we have seen a variety of applications for NLP. One of the most popular applications is a chatbot. Organizations like Zomato, Starbucks, Lyft, and Spotify are leveraging this technology on their website and mobile apps. As a user, we no longer need to worry about being put on hold – just type your query and the chatbot will instantly analyse the text and give the appropriate response.
Recently, I started looking into data sets to compete in Go Code Colorado (check it out if you live in CO). The problem with such diversity in data sets is finding a way to quickly visualize the data and do exploratory analysis. While tools like Tableau make data visualization extremely easy, the data isn’t always properly formatted to be easily consumed. Here’s are a few tips to help speed up your exploratory data analysis!
In this post, you will learn how to: usepdftools to extract text from a PDF, use the stringr package to manipulate strings of text, and create a tidy data set. In anticipation of March Madness and being a University of Cincinnati alumnus along with some other my other Datazar constituents, I have chosen to extract season statistics from the UC men’s basketball team. In the end, I will create a tibble showing season statistics for minutes played, field goal percentage, total points, and average points per game for each player.
In this tutorial, you’ll learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with the TensorFlow framework.
Notebook extensions are plug-ins that you can easily add to your Jupyter notebooks. The best way to install them is to use Jupyter NbExtensions Configurator.
Bitcoin is the first decentralized digital currency. This means it is not governed by any central bank or some other authority. This cryptocurrency was created in 2009 but it became extremely popular in 2017. Some experts call bitcoin “the currency of the future” or even lead it as an example of the social revolution. The bitcoin price has increased several times during the 2017 year. At the same time, it is very volatile. Many economic entities are interested in tools for predicting the bitcoin prices. It is especially important for existing or potential investors and for government structures. The last needs to be ready to significant price movements to prepare a consistent economic policy. So, the demand for Bitcoin price prediction mechanism is high.
I have just come out of a project where 80% into it I felt I had very little. I invested a lot of time and in the end it was a total fiasco. The math that I know or do not know, my ability to write code?—?all of this has been secondary. The way I approached the project was what was broken. I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this. I did a bit of soul searching and went back to what Jeremy Howard mentioned in the wonderful Practical Deep Learning for Coders MOOC by fast.ai and that is how this post was born.
Analytics is becoming important part of maintenance, with applications to analyzing part failures, using failure distributions to simulate product life, and determining the root cause of failures. We provide an overview of predictive maintenance, its usage and key issues to be considered.