This post is part of my forthcoming book The Mathematical Foundations of Data Science. Probability is one of the foundations of machine learning (along with linear algebra and optimization). In this post, we discuss the areas where probability theory could apply in machine learning applications. If you want to know more about…Continue
Added by ajit jaokar on October 27, 2019 at 10:30am — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.
Featured Resources and Technical…Continue
Added by Vincent Granville on October 27, 2019 at 9:00am — No Comments
Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Source: from the Support Vector Machines chapter,…Continue
Added by Capri Granville on October 27, 2019 at 6:30am — No Comments
Another free book to learn Machine Learning. It also comes with a Youtube video series available here.
Added by Capri Granville on October 27, 2019 at 6:30am — No Comments
Some original and very interesting material is presented here, with possible applications in Fintech. No need for a PhD in math to understand this article: I tried to make the presentation as simple as possible, focusing on high-level results rather than technicalities. Yet, professional statisticians and mathematicians, even academic researchers, will find some deep and fascinating results worth further exploring. Source code and Excel spreadsheets are provided for replication…Continue
Which statistical method you use to compare data sets depends on two main factors: your overall goal and the type of data you have. Parametric data means that you know the underlying distribution (for example, your data might be normally distributed).…Continue
Added by Stephanie Glen on October 26, 2019 at 8:45am — No Comments
In the early 2000’s, IBM Deep Blue was on the lookout for its next Grand Challenge. It had achieved an exhilarating win over chess grand master Garry Kasparov in 1997. But that was a game with deterministic outcomes where superior processing power gave Deep Blue a significant advantage over even a human grand champion. IBM needed a challenge commiserate with its Artificial Intelligence aspirations, and the wildly popular TV game show…Continue
Added by Bill Schmarzo on October 26, 2019 at 2:17am — No Comments
Originally posted by Zeeshan Usmani in May 2015.
Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ…Continue
Added by Vincent Granville on October 25, 2019 at 6:25am — No Comments
The process of software development is rapidly evolving with the emerging new technologies being introduced in the field. In order for any software development service to be reliable and successful, it is necessary for the software development team to keep up with these technologies.
Some of the latest tech trends influencing the field…Continue
Added by Hardik Shah on October 25, 2019 at 4:44am — No Comments
To date, there are more than 830,000 data science LinkedIn profiles registered worldwide. Despite this number of Data Scientists available/in roles online currently, it’s no secret there is still a major talent shortage. In fact,…Continue
Added by Matt Reaney on October 25, 2019 at 3:30am — No Comments
Added by Marija Zoldin on October 25, 2019 at 1:30am — No Comments
In a modern world of advanced technologies, our life is literally impossible without cellular telecommunications. Today, smartphones and wireless broadband Internet are common things for everybody. And recently, all developments and innovations in cellular technology are not about the availability of mobile connectivity but about bringing it to a…Continue
Added by David Balaban on October 24, 2019 at 9:32pm — No Comments
Here is our list of featured articles and technical resources posted since Monday:
Added by Vincent Granville on October 24, 2019 at 11:30am — No Comments
Here is a short blog I was asked to make about making a personal Wiki from Wikipedia. It shows the basic steps in text processing so I hope it will be useful for data scientists. It also requires some knowledge of MediaWiki setup on a web server, and some (not very advanced) knowledge of the Python programming language. It takes only several days to create this Wiki with Wikipedia articles if you know…Continue
Added by jwork.ORG on October 24, 2019 at 1:21am — No Comments
As the world continues to collect more and more data with every passing second, it is only understandable that we would need tools that would help us make sense of all the data at our disposal. There are undoubtedly a variety of potent tools that are capable of doing just that; big data has managed to stand out from the crowd. Why? Primarily because it brings with it the promise of just the kind of insights and intelligence businesses need to not only improve their ability to gauge precisely…Continue
Added by Ryan Williamson on October 23, 2019 at 10:02pm — No Comments
What factors contributed to the economic crisis in the United States of America?
The question above is actually also being asked by the people there (USA), and even the world community. With the value of each proportion (in percentage).
There are several things that need to be underlined, including:
Added by Jeefri A. Moka on October 23, 2019 at 9:01am — No Comments
Cybersecurity Data Science (CSDS) is a rapidly emerging profession focused on applying data science to prevent, detect, and remediate expanding and evolving cybersecurity threats. CSDS is increasingly formally recognized as a cybersecurity job specialty, for instance in the NIST NICE Cybersecurity Workforce Framework.
A proposed CSDS definition derived from…Continue
Added by Scott Mongeau on October 22, 2019 at 11:20pm — No Comments
I prefer to structure my code the same way as an article, and if have academic background as well, you can relate. Hence, I usually start with preamble, where I put all the packages and toolkits I would like to use. Then, the main part follows and subsequently the rest (for example, where the result of the model should go, all the connections). Especially if you are getting started and learn python, I recommend to structure and comment your code as clear as possible. With Jupiter you have…Continue
Added by Dr. Katharina Glass on October 22, 2019 at 8:30pm — No Comments
Imagine, you are the first data scientist in a company, may be in the industrial company, in one of the old industries, old economy branches. Then, you are a unicorn. Basically, you start data science from the scratch: you must introduce, explain, promote and establish data science. To manage this challenging task, dare to simple!
Three years ago, I was exactly there – the first and for some time the only data scientist in the traditional, old industry company. The challenge was to…Continue
Added by Dr. Katharina Glass on October 21, 2019 at 9:21pm — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.