Machine learning models make use of training datasets for predictions. And, thus labeled data is an important component for making the machines learning and interpret information. A variety of different data are prepared. They are identified and marked with labels, also often as…

Accuracy, F1, or TPR (a.k.a recall or sensitivity) are well-known and widely used metrics for evaluating and comparing the performance of machine learning based classification.

But, are we sure we evaluate classifiers' performance correctly? Are they or others such as BACC (Balanced Accuracy),…

On the 12th of August, Open AI hosted a hackathon for all those interested in trying out Codex. Codex is a new generation of their GPT-3 algorithm that can translate plain English commands into code.

We at…

Machine learning in retail demand forecasting has transformed the retail industry. The primary aim of using machine learning in demand forecasting is to predict the future demand in products and services accurately so that the product can be designed and re-designed accordingly. It…

We are all aware of the heinous cyber-attack that took down more than 200,000 systems in 150 countries in only a few days in May 2017. This was found by the National Security Agency (NSA) and was nicknamed "WannaCry," which exploited a vulnerability and stole important resources…

By 2035 AI could boost average profitability rates by 38 percent and lead to an economic increase of $14 Trillion.

• Accenture

The words Artificial Intelligence (AI), and algorithms are most often misused and misunderstood. There are often used…

Whenever we begin dealing with machine learning, we often turn to the simpler classification models. In fact, people outside of this sphere have mostly seen those models at work. After all, image recognition has become the poster child of machine learning.…

Among the many decisions you’ll have to make when building a predictive model is whether your business problem is either a classification or an approximation task. It’s an important decision because it determines which group of methods you choose to create a model: classification (decision trees, Naive Bayes) or approximation (regression tree,…

Source: Google

Artificial intelligence is among the most talked-about trends in recent years, and It is a 2 side blade with positives and negatives. Talking about positives, technologies are starting to improve our lives in multiple ways, from enhancing the healthcare…

Build a Continuously Adaptive Learning Organization (CALO)

Among the most perplexing challenges facing CEOs in…

We are currently living in such a period where computing has transformed immensely from large mainframes to personal computers to the cloud.  The constant technological progress and the evolution in computing resulted in major automation.

In this article, let’s understand few commonly used…

Artificial intelligence systems usually learn by example and are likely to learn better with high-quality examples. Low quality or insufficient training data can lead to unreliable systems that make poor decisions, reach the wrong conclusions, introduce or perpetuate bias and cannot handle real-world variation among other issues. Besides, poor data is…

We all experience video streaming issues, probably on a daily basis. But now that people aren’t just streaming for entertainment, but are relying on it in many cases to perform their jobs, it ups the ante to address some of these issues and provide an improved experience. Deep…

Artificial Intelligence (AI) is all the buzz with everyone looking for ways to leverage AI in some capacity within their organization. Whether you’re running a business focused on insurance, construction, or anything in between, the list of companies adding AI or Data Science teams to help their business succeed is growing every day. Unfortunately for…

Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and the insurance industry specifically. That said, there is still much more territory to cover, helping…

According to global smart learning Industry analysis by Research Dive, the global industry forecast will be $74,179.1 million in 2026, at a 19.2% CAGR and is growing from $18,200.0 million in2018.

Smart Learning industry Drivers: Growing acceptance of e-learning in academic & corporate sectors…

Introduction and Motivation

AI is taking over. In 2021, we’re going to see machine learning taking on a bigger role in data analysis as algorithms become capable of reducing error and producing more accurate models within themselves. These algorithms cover iterative processes, decision trees as well as multi-dimensional splitting of datasets. Machine learning is enabling data analysts to have new and greater insights, affecting everything from marketing departments to the way we learn. Here are the essential machine…

Summary:  Things are getting repetitious and that can be boring.  Still, looking at lessons from the 90s it’s clear there are at least one or two decades of important economic advances that will based on our current AI/ML.  Then some thoughts on where that next really huge breakthrough will come from that will return our initial excitement.

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Summary:  There is now sufficient experience among mid and large sized companies starting their AI journey to identify a single best practice for moving from AI experimentation to scale-up: the AI COE (Center of Excellence).

If you are a mid-sized business, government organization, or educational…