The popularity of open-world games has grown over the last few years. Much of this is due to their immersive worlds, which tend to be so vast that players could spend hours just walking around enjoying the slightest details. Some of these include games such as The Witcher 3: Wild Hunt, Elden Ring, Elder Scrolls V: Skyrim, and many others. But, with a massive and immersive world, comes the need for non-playable characters, or NPCs, who are a valuable aspect of open-world games and this is where Ghostwriter comes in. For many who are outside of the gaming industry, the chatter is just that, background noise that helps your mind believe it's in another world.

Creating a churn propensity model is now pretty standard for data scientists. Today, churn is the most common data science problem in the world, because every company wants recurring revenue. But how do you go from a churn model to churn prevention? It is much harder than it sounds. Suppose you have a machine learning model that can predict churn.

A data science model development pipeline involves various components including data injection, data preprocessing, feature engineering, feature scaling, and modeling. A data scientist needs to write the learning and inference code for all the components. The code structure sometimes becomes messier and difficult to interpret for other team members, for machine learning projects with heterogeneous data. A pipeline is a very handy function that can sequentially ensemble all your model development components. Using a pipeline one can easily perform the learning and inference tasks in a comparatively cleaner code structure.

According to Machine Learning models, you'll be denied a loan if you have a PhD, but not if you only have a BA. Can we change this outcome? Learn how to audit your ML models to understand and improve their decision factors.

Generative Adversarial Networks are transforming what we're able to do with neural networks, and it's unfortunate that almost all the press goes to those wildly accurate facial constructions like that of This Person Does Not Exist. GANs have some incredible potential so let's take a look at some really unique use cases that display the power of this advancement. For those of you non-data scientists, GANs are a type of neural network that relies on two different components, one to generate content (adaptive network) and the other to test it (discriminator)– thus "generative adversarial.' The discriminative network attempt to distinguish between real and generated content, helping the generator learn through each iteration. It's a type of unsupervised training in which the adaptive network attempts to fool the discriminator, improving accuracy and helping the machine learn what constitutes an acceptable degree of accuracy.

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Whitepaper: The Journey to Open Data Science - Migrating from traditional analytics to modern Open Data Science (ODS) is one of the most important trends of the decade. However, although this migration is critical to the core mission of many enterprises, practicing Data Science effectively has remained a thorny challenge.

Data science is a team activity. Ideally, you want to build a team in which diverse specialists pool their skills and knowledge, wield a core set of sophisticated productivity tools and collaborate flexibly and efficiently. Data scientists produce a steady stream of machine-learning, predictive, segmentation and other advanced analytics models. As a team, its effectiveness depends on having advanced tools such as Apache Spark and languages such as R and Python at their disposal. It also depends on having tools to support creative design, agile collaboration and workflow management of data, algorithms, models and other artifacts.