Unsupervised learning algorithms are "unsupervised" because you let them run without direct supervision. You feed the data into the algorithm, and the algorithm figures out the patterns.  The following picture shows the differences between three of the most popular unsupervised learning algorithms: Principal Component Analysis,…

Why is data so important?

Despite being about as prevalent as electricity, it can be difficult to adequately explain how critical data is to the modern world. From business operations to tackling the environmental crisis, data is the key to unlocking insight and developing intelligent solutions across every sector. Although Big Data has been in the news for at least a couple of decades, other types of data are now getting air time as well. Open…

Automatic Adjoint Differentiation (AAD) and back-propagation are key technologies in modern machine learning and finance. It is back-prop that enables deep neural networks to learn to identify faces on photographs in reasonable time. It is AAD that allows financial institutions to compute the risks of complex derivatives books in real time. The two technologies share common roots.

See the AAD book here:…

Running a business is no easy task, for there are far too many that need to be done and managed every single day. And the thing is that a significant chunk of these daily duties involves processes that are not only mundane but also repetitive in nature. It is especially true in the context of documentation and management of similar assets. Nonetheless, they must be tended to fairly regularly, if not every single day, to make sure that the business continues to move as it is intended to be.…

by Hudson Hollister

Detecting which of the federal government’s millions of contracts most likely involve fraud used to require insider access to agencies’ IT systems. Data analytics provides greater efficacy and higher hit rate than traditional investigative methods – and now can even be performed using only public…

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

 …

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…

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…

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,…

Another free book to learn Machine Learning. It also comes with a Youtube video series available here. 

Content

• Machine Learning Setup
• k-Nearest Neighbors / Curse of…

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…

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). Non parametric tests are an…

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…

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…

Why Data Scientists Should Be Using LinkedIn

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,…

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…

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…

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…

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 an 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 start 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…

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.

“The Next Big Thing in AI/ML is…” as the lead to an article is probably the most…