AI bias is in the news – and it’s a hard problem to solve

But what about the other way round?

When AI engages with humans – how does AI know what humans really…

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…

Bayesian Machine Learning (part - 2)

Bayesian Way Of Linear Regression

Now that we have an understanding of Baye’s Rule, we will move ahead and try to use it to analyze linear regression models. To start with let us first define linear regression model…

The sheer number of model evaluation techniques available to asses how good your model is can be completely overwhelming.  As well as the oft-used confidence intervals, confusion matrix and…

Do you old schoolers remember the Shimmer Floor Waxcommercial from the early (and best) days of the TV show “Saturday Night Live”?  In case folks don’t remember (because we are old) or never saw it (because you’re too uncool), the Shimmer Floor Wax commercial asked the question: “Is Shimmer a floor wax or a dessert topping?”…

This online book is intended for beginners, college students and professionals confronted with statistical analyses. It is also a refresher for professional statisticians. The book covers over 600 concepts, chosen out of more than 1,500 for their popularity. Entries are listed in alphabetical order, and broken down into 18 parts. In addition to numerous illustrations, we have added 100 topics not covered in our online series Statistical Concepts Explained in Simple English. We also…

With the increasing popularity of Machine learning today, it is important to focus the testing aspect of ML application. Testing of any ML application will not be same as testing traditional software. It has become a debatable topic. Many literature categorised ML application as non-testable. However, many are now trying to make it testable and coming up many innovative approaches. All are very much technical and normal users without technical knowledge find difficulties. Therefore,…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

We are entering into the age of IOT, lot of connected devices will talk to each other using sensors/signals and it is expected that those devices will generate an enormous amount of data.

Handling that much of data [Big data] and generating actionable signals from that would be a challenge.

Companies are investing a lot of…

Let me start off with a few common assumptions about the subject of this write up "Four Powerful People Skills For Data Scientists", and my take on each of them based on my experience and observations.

1. Common Assumption: People skills are just "fluffy" soft skills

Soft skills are often referred to as "the fluffy stuff". People skills in particular, top the list of fluffiness, in many people's…

In this digital age, the threats against an organization’s data are massive and the consequences of a breach are extremely devastating for a business. So it has become important to consider various factors when it comes to secure databases.

Our world is on a huge risk of data theft and a few months ago, Facebook data breach news proved this.…

The sports digital transformation is upon us, and the world of sports is about to be disrupted through the technology at play. In the words of the most decorated Olympian to step on the podium:

“You can’t put a limit on anything. The more you…

When one looks at the amazing roster of talks for most data science conferences what you don’t see is a lot of discussion on how to leverage object storage. On some level you would expect to — ultimately if you want to run your Spark or Presto job on peta-scale data sets and have it be available to your applications in the public or private cloud — this would be the logical storage architecture.

While logical, there has been a catch, at least historically, and that is object storage…

OVERVIEW & PURPOSE

Before we dive deep into the topic let's ask a few questions to yourself and start diving into it as deep as you want to…!!!

1. What it is?
2. Why it came into existence?
3. Where it was used?
4. What are the results we achieve using this?
5. How we can implement this?

Coming to the…

Abstract

The “new square method” is an improved approach based on the “least square method”. It calculates not only the constants and coefficients but also the variables’ power values in a model in the course of data regression calculations, thus bringing about a simpler and more accurate calculation for non-linear data regression processes.

Preface

In non-linear data regression calculations, the “least square…

Nowadays, lot's of discussion is happening around the question of what artificial intelligence can and can't do. Even though artificial intelligence has a controversial status, this technology already has some real-life business applications and delivers proven results.

Building a meaningful interaction with…

Wait, the AI advantage is already here and gone? 

That’s what Deloitte warns in their report “Future in the balance? How countries are pursuing an AI advantage”.A noteworthy quote:

“There are indications that the window for competitive differentiation with AI is rapidly closing. As AI technologies become easier to consume and get embedded in…

Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.

I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the…