Machine Learning
Machine learning theory deals with the problem of finding a predictive function based on data. Machine learning theory has led to successful applications in many fields of business and technology.
Its a science which gives you reasons for your decisions.
            The significant amount of corporate information available requires a systematic and analytical approach to select the most important information and anticipate major events.

            Machine learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major corporate variables.
Machine Learning:

          Machine learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset.

          In business settings, data scientists who perform machine learning do not create an AI in most cases. Instead, they may create a ‘bare bones’ predictive model without any additional bells and whistles. In these instances, the machine does not make an automatic decision; an educated, trained expert makes the final call. The model is used as a decision-making aid.

          The science of machine learning plays a key role in the fields of statistics, data mining and artificial intelligence, intersecting with areas of engineering and other disciplines. It also has great applications in fields as diverse as business, medicine, astrophysics, and public policy.
Our Machine Learning techniques:
Tools Expertise:
Linear methods: regression, logistic regression (binary and multiclass), Cox model.
Bootstrap, cross-validation, and permutation methods.
Regularized linear models: Ridge, Lasso, Elastic net
Post-selection inference
Trees, random forests, and boosting.
Discriminant Analysis
Unsupervised methods: clustering (prototype, hierarchical, spectral)
Low-rank methods
Sparse decompositions
Support-vector machines and kernel methods
Active learning
Reinforcement learning
Principal Component Analysis
Principal curve
Factor analysis
Independent Component Analysis
Jarvis & Patrick clustering
Feature selection
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Ai > Decision Science