Machine learning is the field of Artificial Intelligence in which the subsystems are programmed to receive historical data and form results, which can be used to make decisions.
Classical problems of machine learning are:
- Regression (Approximation)
- Representation learning
- Metric learning
- Machine-learned ranking
- Order Relation
We use different methods of learning and data collection to create solutions for our customers. In the work process, we specify the features of data sets and proceed to learn or conduct prior data conversion to obtain appropriate format. Sometimes, it is necessary to take time and carry out experiments to find out which method suits best. That is why we offer the following plan of work:
1. We start working with a simple algorithm and reasonable amount of data;
2. Create leaning characteristics outline to see how it works and, if necessary, use more data.
3. Analyze mistakes and define a pattern which allows improving the algorithm.
While working with machine learning, we use R and Python languages, and also such tools and libraries:
- Tenzor Flow
- IBM Watson