The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist…

In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…

Summary:  IBM’s Watson QAM (Question Answering Machine), famous for its 2011 Jeopardy win was supposed to bring huge payoffs in healthcare.  Instead both IBM and its Watson Healthcare customers are rapidly paring back these projects that have largely failed to pay off.  Watson was the first big out-of-the-box commercial application in ML/AI.  Has it become obsolete?

  …

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms.

Machine learning is making significant inroads in the financial services industry. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly…

The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons — machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts.

And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they…

Amazon is every online retailer’s forbidding nightmare. Last year, it dominated 44 percent of the US eCommerce market and about 4 percent of all domestic retail sales. One Click Retail, an eCommerce analysis provider, explains its dominance with the fact that millennials, Amazon’s core demographic, are getting older and starting to spend more. Moreover, advanced marketing capabilities for sellers, developments in Alexa, and pioneering in applications of the hottest technologies make it…

What is an artificial intelligence (AI)?

Most of us can not imagine a single day without a computer. With the rapid development of technology, various devices that simplify people's lives become more accessible. This is also connected with modern computers, which are capable of providing impressive fast processing of information.

     Modern business uses the full potential of information technology. This allows you to store important data and manage it…

The book "Mastering Machine Learning Algorithms" has been published by Packt 

From the back cover:

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more…

In a previous blog I wrote about 6 potential applications of time series data. To recap, they are the following:

1. Trend analysis
2. Outlier/anomaly detection
3. Examining shocks/unexpected variation
4. Association analysis
5. Forecasting
6. Predictive analytics

Here I am focusing on outlier…

Machine learning has the ability to automate a lot of jobs in the future. It is very easy to talk about this automation when it isn't your job that will be automated. But the scary part is that there are a lot of highly skilled jobs that will also face some type of automation in the future as well. When you are talking about your own job potentially being automated, it becomes less abstract and more real. It is very easy to say go ahead and automate jobs, until it is your own that is being…

1. Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on…

How good is a certain soccer player? Let’s find out applying Machine Learning to Fifa 18!

I’m sure you’ve probably heard about the 2018 FIFA Football World Cup in Russia everywhere during the last few months. And, if you are a techy too, I guess you also have realized that Machine Learning and Artificial Intelligence are buzzwords too. So, what better way to get ready for the World Cup than by practicing in a project that combines these two hot…

"No one wants to be sold but everyone wants to buy."

Most of us hate being sold. The moment we know someone is selling something, we keep our guards up. 

In the book, The Challenger Sale, authors Mathew Dixon and Brent Adamson surveyed over 6000 salespeople from around the world and found that ‘challenger salespeople’ outperformed every other group. Who are these challenger salespeople? These…

Time series forecasting is hardly a new problem in data science and statistics. The term is self-explanatory and has been on business analysts’ agenda for decades now: The very first practices of time series analysis and forecasting trace back to the early 1920s.

The underlying idea of time series forecasting is to look at historical data from the time perspective, define the patterns, and yield short or long-term predictions on how –  considering the captured patterns – target…

Introduction

Getting Your Head Around Data Science

Data science has made its way into practically all facets of society – from retail and marketing, to travel and hospitality, to finance and insurance, to sports and entertainment, to defense, homeland security, cyber, and beyond. It is clear that data science has successfully sold its claim of "actionable insights from…

In the past few years, machine learning (ML) has revolutionized the way we do business. A disruptive breakthrough that differentiates machine learning from other approaches to automation is a step away from the rules-based programming.  ML algorithms allowed engineers to leverage data without explicitly programming machines to follow specific paths of problem-solving. Instead, machines themselves arrive at the right answers based on the data they have. This capability made business…

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference.

To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic. And it still doesn't cease to amaze me. After all, we are predicting the future. Take the case of Nate Silver - What else can you call his success if not Black…

The research firm Technology Business Research (TBR) recently came out with a report titled, “Winning The Business Of Digital Transformation Services Requires A Process-Led Approach” authored by Sebastian Lagana and Jennifer Hamel. The report is full of good nuggets, but I especially liked the way that they categorized the 3 phases of Digital Transformation:

When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don’t ease the choice.

In this article, I will try to explain basic concepts and give some intuition of using different…