Articles

Generating Business Insights Using Advanced Analytics

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Machine learning-based applications have seen significant commercial success in several mainstream consumer applications in the recent past. Self-driving cars, stock-trading bots, robo-advisors, Amazon’s Alexa, and Apple’s Deep Fusion and Siri are some of the renowned examples of commercial success with artificial intelligence and machine learning. AI has also made our lives easier by improving the customer experience of the products we use. Google’s text generation software, Netflix’s recommendation engine, and Facebook and Twitter’s fake news detection are other prime examples. In fact, every single technology company uses AI in its mainstream applications either directly or indirectly. Non-technology companies are also using AI to improve customer experience, improve efficiency, and generate new revenue streams. Chatbots, robo-advisors, systems that predict system failures, and products that generate efficient supply chain routes are some of the prominent ways in which non-technology companies use AI. This is leads to a popular belief that AI and ML are primarily used by technology companies or they are being used by non-tech companies to build AI-based products.

This popular perception is not true. There are plenty of avenues in which AI/ ML is being used or can be used by non-tech and non-product-based groups to generate insights. In this article, I am going to share with you four ways in which you can augment advanced analytics into your analytics strategy to generate insights.

Source de l’article sur DZONE

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5 NLP Trends to Watch in 2021

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Although still in its infancy, 2020 has been a year of significant growth for Natural Language Processing (NLP). In fact, research from Gradient Flow found that even in the wake of the COVID-19 pandemic, 53% of technical leaders indicated their NLP budget was at least 10% higher compared to 2019, with 31% stating their budget was at least 30% higher than the previous year. This is quite significant, given most companies are experiencing a downturn in IT budgets, as companies adjusted their spending in response to the pandemic. 

With the power to help streamline and even automate tasks across industries, from finance and healthcare to retail and sales, leaders are just beginning to reap the benefits of NLP. As the technology advances further and its value becomes more widely known, NLP can achieve outcomes from handling customer service queries to more mission-critical tasks, like detecting and preventing adverse drug events in a clinical setting. As NLP continues on its growth trajectory, here are some of the top trends to watch in 2021. 

Source de l’article sur DZONE

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The Importance of Machine Learning Security for Chatbots

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Artificial Intelligence is a growing industry powered by advancements from large tech companies, new startups, and university research teams alike. While AI technology is advancing at a good pace, the regulations and failsafes around machine learning security are an entirely different story.

Failure to protect your ML models from cyber attacks such as data poisoning can be extremely costly. Chatbot vulnerabilities can even result in the theft of private user data. In this article, we’ll look at the importance of machine learning cyber security. Furthermore, we’ll explain how Scanta, an ML security company, protects Chatbots through their Virtual Assistant Shield. 

Source de l’article sur DZONE

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3 Ways to Select Features Using Machine Learning Algorithms in Python

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Artificial intelligence which gives machines the ability to think and behave like humans are gaining traction since the last decade. These features of artificial intelligence are only there because of its ability to predict certain things accurately, these predictions are based upon one certain technology which we know as machine learning (ML). Machine learning as the name suggests is the computer’s ability to learn new things and improve its functionality over time. The main focus of machine learning is on the development of computer programs that are capable of accessing data and using it to learn for themselves. 

To implement machine learning algorithms, two programming languages, R and Python for machine learning are normally used. Generally, selecting features for training data on machine learning in python is a very complex and technical process. But here we will go over some basic techniques and details regarding what is machine learning and how it works. So, let us start by going into detail regarding what ML is, what feature selection is and how can one select feature using python.

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Gradient Descent vs Normal Equation for Regression Problems

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                                                      Gradient Descent v/s Normal Equation

In this article, we will see the actual difference between gradient descent and the normal equation in a practical approach. Most of the newbie machine learning enthusiasts learn about gradient descent during the linear regression and move further without even knowing about the most underestimated Normal Equation that is far less complex and provides very good results for small to medium size datasets.

If you are new to machine learning, or not familiar with a normal equation or gradient descent, don’t worry I’ll try my best to explain these in layman’s terms. So, I will start by explaining a little about the regression problem.

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K-Means and SOM: Introduction to Popular Clustering Algorithms

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K-means and Kohonen SOM are two of the most widely applied data clustering algorithms.  

Although K-means is a simple vector quantization method and Kohonen SOM is a neural network model, they’re remarkably similar. 

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Finding the Humanity of Big Data

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It’s no secret that Big Data offerings have become one of the largest marketing bastions the world has ever seen.

In a fast-paced and ever-changing era, industries race against one another more than ever before to raise benchmarks, contexts, ROI, and ultimately profit margins in an interconnected world that never sleeps. Big data consulting services have been around for several years now, helping organizations reach their business goals by carefully absorbing and organizing trillions of bytes worth of data. As the process progresses and internet access continues to expand around the globe, the amount of data to process will only continue to swell.

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6 Pillars of a Successful AI Strategy

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Machine learning and artificial intelligence, in general, have been on everyone’s lips for some time now. While the topic of AI is in the foreground in the media, most people (especially the management) still don’t know how machine learning is best applied.

Ultimately, machine learning can be described as a synergetic relationship between man and machine. Machine learning in practice requires the application of the scientific method and human communication skills. Successful companies have the analytical infrastructure, know-how, and close collaboration between analysts and business professionals to translate these synergies into ROI.

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Real-Time Machine Learning With TensorFlow in Data Collector

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The real value of a modern DataOps platform is realized only when business users and applications are able to access raw and aggregated data from a range of sources, and produce data-driven insights in a timely manner. And with Machine Learning (ML), analysts and data scientists can leverage historical data to help make better, data-driven business decisions-offline and in real-time using technologies such as TensorFlow.

In this post, you will learn how to use TensorFlow (TF) models for prediction and classification using the newly released TensorFlow Evaluator* in StreamSets Data Collector 3.5.0 and StreamSets Data Collector Edge.

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Fake News’ Foe: Machine Learning and Twilio

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Fake news has become a huge issue in our digitally-connected world and it is no longer limited to little squabbles — fake news spreads like wildfire and is impacting millions of people every day.

How do you deal with such a sensitive issue? Countless articles are being churned out every day on the internet — how do you tell real from fake? It’s not as easy as turning to a simple fact-checker which is typically built on a story-by-story basis. As developers, can we turn to machine learning?

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