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.
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.
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.
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.
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.
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.