Machine learning is an important part of modern application development, replacing much of what used to be done using a complex series of rules engines, and expanding coverage to a much wider set of problems. Services like Azure’s Cognitive Services provide prebuilt, pretrained models that support many common use cases, but many more need custom model development.
Going custom with ML
How do we go about building custom machine learning models? You can start at one end using statistical analysis languages like R to build and validate models, where you’ve already got a feel for the underlying structure of your data, or you can work with the linear algebra features of Python’s Anaconda suite.
