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.
Inverse Reinforcement Learning: Use Cases & Examples
Inverse reinforcement learning is an approach in machine learning where machines infer the goals or reward structures that guide an expert’s behavior by observing their actions rather than receiving explicit instructions. Discover what inverse reinforcement learning is, how it works, and the top industry use cases with examples.
Toloka AI Review & Its Top Alternatives for RLHF
Toloka AI is a popular name in the Reinforcement Learning from Human Feedback (RLHF) and AI data services spaces. If your business is considering an RLHF or AI data partner like Toloka AI, our research can provide valuable guidance.
Machine Learning Accuracy: True-False Positive/Negative
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.
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.