If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
When it comes to data science solutions, there's always a need for fast prototyping. Be it a sophisticated face recognition algorithm or a simple regression model, having a model that allows you to easily test and validate ideas is incredibly valuable. Many data science problems out there require specially crafted solutions due to their complicated nature. This means that the data scientists working on these problems will eventually need to improvise on the issue. Not having to wait to calculate some additional feature column on the dataset every time you execute your script becomes a crucial gain in terms of productivity.
Like any other language or tool, Python has some best practices to follow before, during, and after the process of writing your code. These make the code readable and create a standard across the industry. Other developers working on the project should be able to read and understand your code. We have listed out a few of these for you to follow and write cleaner and more professional code. Do you follow any of these?
Dashboard gives a graphical interface to visualize the key indicators and trends of your data. They have to wear a Front-end Dev hat for creating a Web Dashboard. Switching gears from python to Javscript/HTML is not what all developers prefer to do. Somehow I didn't find all these options really an easy available options to create a dashboard in Python so my Search for easy to use and a user friendly Library in pure python continues and Finally I found something much easier way to do that. The good part is that it comes with an in-built web server too and can be easily deployed inside a docker container.
There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.
One of the common problems people face in learning R is lack of a structured path. They don't know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time.
The IPython notebook is an interactive, web-based environment that allows one to combine code, text and graphics into one unified document. All of the lectures in this course have been developed using this tool. In this lecture, we will introduce the notebook interface and demonstrate some of its features. New: A new version of the IPython notebook knowan as Jupyter supports multiple kernels (differnet languages) and other enhancements. For a tour of its features, see this notebook.
With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers' revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.
A big thank you to those of you who have been following the blog for some time now, and welcome to all of you joining for the first time in 2017! I spent the holiday break fine-tuning my writing and publishing process. The biggest difference regular readers will notice is that I've figured out a way to keep writing in Markdown, but have LaTeX math rendered in the blog as well as in the email newsletter (where I can't rely on the MathJax.js Previously I've been writing math expressions using plain HTML, which gets quite tedious, and also makes it hard to include some formulas! The LaTeX version looks much better, especially on the blog site.
This post is in reply to a request: How did I set up this R blog? I have wanted to have my own R blog for a while before I actually went ahead and realised this page. I had seen all the cool things people do with R by following R-bloggers and reading their newsletter every day! While I am using R every day at work, the thematic scope there is of course very bioinformatics-centric (with a little bit of Machine Learning and lots of visualization and statistics), I wanted to have an incentive to regularly explore other types of analyses and other types of data that I don't normally work with. I have also very often benefited from other people's published code in that it gave me ideas for my own work and I hope that sharing my own analyses will inspire others as much as I often am by what can be be done with data.