We provide expertise in deep learning architectures and machine learning algorithms
Data Science in Mining
Knowledge Discovery involves unveiling meaningful, actionable, and consistent patterns and connections within vast datasets. By using tailored methods suited to the dataset's nature, we uncover significant hidden attributes. APMT offers expertise in knowledge discovery through:
Statistical learning involves deciphering the statistical correlations among several variables to establish precise predictive models. This understanding can manifest through unsupervised, (semi)supervised, supervised, and reinforced learning techniques. At APMT, our aim is to offer models that are both intuitively comprehensible and exhibit top-tier accuracy, anchored in:
OPTIMAL SELECTION OF ML/DL ARCHITECTURES
Machine Learning (ML), with its specialized subset Deep Learning (DL), offers an expansive suite of techniques for tasks like classification, regression, and forecasting. Given their sensitivity to data context and workflow representation, a performance evaluation is crucial before their real-world deployment to ensure informed decision-making. At APMT, we derive the best predictive models through:
ENSEMBLE ML/DL METHODS
In the mining sector, there are instances where a singular predictive model doesn't fully address our objectives. Ensemble methods frequently outperform by integrating multiple ML/DL frameworks into a coherent workflow, culminating in a singular dependable predictor. At APMT, we merge mining operational insights with predictive frameworks to deliver ideal ensemble workflows and steadfast predictions.