Machine learning is applied to a wide range of business tasks – from detecting frauds to selecting the target audience and product recommendations, as well as monitoring production in real time, analyzing the tonality of texts and medical diagnostics. It can take over the tasks that can not be performed manually due to the huge amount of data to be processed. In case of a large set of data, machine learning sometimes reveals non-obvious dependencies that cannot be detected by an arbitrary rigorous manual examination. In this case, the combination of the set of such “weak” relations gives perfectly functioning forecasting mechanisms.
The process of learning from the data and the subsequent application of the knowledge to justify future decisions is an extremely powerful tool. Machine learning quickly turns into the engine of a modern, data-driven economy.
In recent years, machine learning (‘ML’) has turned into a large business – companies use it to earn money. Applied research is rapidly developing both in industrial and academic environments, and curious developers everywhere are looking for an opportunity to raise their experience level in this field. Nevertheless, the emerging demand far exceeds the speed of the appearance of good techniques and tools.
In this post, we would like to describe how you can use Microsoft Azure Machine Learning Studio to build machine learning models, as well as what problems you might encounter while using Azure ML and how to get around them. Read more