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Data Science

Data science empowers organizations to extract actionable insights from data through statistical analysis, machine learning, and predictive modeling. We explore tools, techniques, real-world applications, and best practices to support data-driven decision-making and digital transformation efforts.

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Machine Learning Accuracy: True-False Positive/Negative

Data ScienceJun 23

Selecting the right metric to evaluate your machine learning classification model is crucial for business success. While accuracy, precision, recall, and AUC-ROC are common measurements, each reveals different aspects of model performance. We’ve analyzed these metrics to help you choose the most appropriate one for your specific use case, ensuring your models deliver real value.

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Data ScienceJun 13

Guide To Machine Learning Data Governance

In this article, we explain machine learning data governance. We explain its key principles, benefits, use cases, best practices, and our future expectations of data governance.

Data ScienceJun 11

Meta Learning: 7 Techniques & Use Cases

Training and fine-tuning a typical machine learning (ML) model can take weeks and cost thousands. Meta learning helps cut this down by leveraging prior learning experiences to accelerate training, reduce costs, and improve generalization. Explore key meta learning techniques and use cases in fields like healthcare and online learning.

Data ScienceMay 28

Applying RLHF: Techniques, use cases, and challenges

Training AI systems to align with human values can be a challenge in machine learning. To mitigate this, developers are advancing AI through reinforcement learning (RL), allowing systems to learn from their actions. A notable trend in RL is Reinforcement Learning from Human Feedback (RLHF), which combines human insights with algorithms for efficient AI training.

Data ScienceMay 27

Few-Shot Learning: Methods & Applications

Imagine a healthcare startup developing an AI system to detect rare diseases, but there’s a problem: there’s not enough labeled data to train a traditional machine learning model. This is where few-shot learning (FSL) comes in.

Data ScienceApr 4

Web Scraping for Machine Learning: From HTML to ML

~54.7 billion people around the world have been recorded to use the internet, creating 1.7MB of data every second. Crawling this exponentially growing volume of data could provide many opportunities for breakthroughs in data science.