It’s common for people to ask us what is the difference between Business Intelligence and Machine Learning. I also asked myself that question when I started in this exciting world of data-based predictions.
Prediction of churning is one of the best known applications in the field of Machine Learning, Big Data and Data Prediction.
Data-based prediction technologies have been simplified so much that they have been made available not only for big companies, even to those of any size. Tools such as those developed by BigML are bringing Machine Learning closer to companies, but it should not be forgotten that the raw material for any predictive system is data.
When people first see a demo of a Machine Learning product, there is a general feeling that it is something magical. Probably because it is a different way of seeing how computers work. Instead of using closed code, which behaves like a calculator that always throws the same result when the input data is identical, a Machine Learning system works with the patterns it discovers from the data as it’s fed with them. These are dynamic programs that change over time and from which you may not obtain the same results over time even when the input data is the same.
In a conference, Danny Lange – Director of Machine Learning at Uber – made clear his opinion: Machine Learning must be taken to every corner of the company. Let’s not forget he led the Machine Learning team at Amazon. Let’s remember that Amazon has taken Machine Learning to all its areas to do interesting things such as predicting the demand for its products, setting their prices, making personalized recommendations, optimizing distribution routes, improving computer vision or detecting fraud. Last year they went a step further and created a cloud platform to bring Machine Learning capabilities to all companies.
Recientemente Amazon anunció sus planes de abrir un centro de Investigación y Desarrollo (I+D) en Barcelona dedicado al aprendizaje automático o Machine Learning. El centro se ubicará en el distrito tecnológico del 22@, su apertura será durante el primer semestre del 2018 y se prevé la contratación de más de 100 científicos e ingenieros en los próximos años.
Machine Learning technologies are making the leap from the academic world and gaining strength in the business one. Nowadays anyone can use them to put their data to work and achieve competitive advantages that until recently, were only available to large companies and institutions.
We have compiled some ideas and basic concepts of Machine Learning
Machine Learning is a scientific discipline in the field of Artificial Intelligence that creates systems able to learn automatically. Learning in this context means identifying complex patterns in millions of data. The machine that really learns is an algorithm that reviews the data and is able to predict future behavior. Automatically, also in this context, implies that these systems are improved autonomously over time, without human intervention. Let’s see how it works.
Creating a high-performance model is not easy. Efforts are usually focused in selecting the Machine Learning algorithm that best explains the data or in tweaking the algorithm parameters to select those that produce the best results. But I think that the secret for improving a Machine Learning model lies in the previous steps, those where the data is engineered before the learning phase.
Empecemos con una nota de optimismo. La crisis global, la recesión económica de los últimos años, al menos en su manifestación más acentuada, es parte de la historia. En términos generales, es manifiesta la trayectoria de recuperación de las economías avanzadas (si aceptamos este calificativo), así como el crecimiento de las emergentes.