Machine learning development company

We specialise in various ML methodologies such as Supervised learning through Regression and Classification, unsupervised through Clustering and Dimensionality Reduction, Reinforcement Learning and Deep Learning through Neural Networks, CNNs, RNNs. Advanced techniques in which our experience stretches out are Transfer Learning and Ensemble Methods

We evaluate the requirements of the project, data characteristics, and problem type to choose the most appropriate ML model. We go through a variety of algorithms, looking at model performance with such metrics as accuracy, precision, recall, F1 score, etc., and techniques like cross-validation for the robustness of the model.

Data preprocessing shall include the cleaning of data, handling of missing values and outliers, feature engineering, creation of new features, and selection of important ones, normalization/standardization, and lastly, data augmentation. This step goes a long way in ensuring better model performance and accurate prediction.

We apply various kinds of evaluation techniques, including train-test splits, cross-validation, and performance metrics specific to the domain of a problem, such as ROC-AUC for classification and RMSE for regression. We also do error analysis and confusion matrix evaluations for the refinement of model performance.

We use a diversity of tools and frameworks for model development, such as TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost. In developing tools and frameworks for data manipulation and visualization, we majorly use Pandas, NumPy, and Matplotlib. Our choice of tools will therefore depend on the requirements of the projects and performance considerations.