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Generative Engine Optimization (GEO) for Modern B2B Visibility: An Actionable Guide
How to safeguard against leaking sensitive PII data while allowing their employees to use LLM models and other 3rd party AI tools.
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WDIS AI-ML Series: Module 2 Lesson 6: Model Selection and Evaluation Metrics
In most practical applications, data scientists often have a set of ML models that can be applied to solve a problem. Data scientists run a set of ML models and see which ones perform the best. This is called Racing ML models against each other to choose a winner.
NeutoAI CoMarketer - Marketing Meets AI with Adaptive Content Optimization (ACO) System
NeutoAI CoMarketer introduces Adaptive Content Optimization (ACO), seamlessly merging AI and marketing for dynamic, personalized campaigns.
Protecting Sensitive Data in the Age of Large Language Models (LLMs)
How to safeguard against leaking sensitive PII data while allowing their employees to use LLM models and other 3rd party AI tools.
WDIS AI-ML Series: Module 2 Lesson 5: Feature Extraction, Feature Selection & Feature Engineering Techniques
It is an initial phase of any data science project, is a critical step in the data analysis process, used to understand the underlying structure, patterns, and relationships within a dataset before formal modeling or hypothesis testing. It's like detective work, where you delve into your data to understand its characteristics, identify patterns, and uncover potential insights.
WDIS AI-ML Series: Module 2 Lesson 4: Data Collection and Data Preprocessing
Not many companies invest enough in data as much as they do in Data Science. Albeit the realization is growing that to be seen as an ‘AI-first’ company, one needs to establish itself as a ‘Data-first’ company. The biggest challenge In this section we will give an overview of what end-to-end data processing looks like from the viewpoint of a data science project:
