Archive for ‘Machine Learning’ Category

04
Oct
Deep Learning from the basis
  • Pablo Navarro
  • 348 Views
  • 0 Comment
  • Artificial Intelligence . Deep Learning . Inteligencia Artificial . Machine Learning . Neural Networks . Perceptrones . Redes Neuronales .

As mentioned in the previous article “Deep Learning, only for professional pilots”: there is no point in buying a Ferrari with a year´s license. However, we want to evoke in favor of Deep Learning and we thought it's important to start somewhere. We want to start with a purely informative article, for those to whom Deep Learning sounds like a science fiction or a horror movie. We do not pretend you end up using neural network models on a daily basis, we are just interested that you have the base to be able to make the leap into the world of Deep Learning. Starting from the basics, it is convenient to ask ourselves the question: What is Deep Learning? The…

29
Aug
Semi-Supervised Learning… the great unknown
  • Alfonso Ibañez
  • 3374 Views
  • 0 Comment
  • Algoritmos Semisupervisados . Big Data . Machine Learning .

In recent years much progress has been made in solving complex problems thanks to Artificial Intelligence algorithms. These algorithms need a large volume of information to discover and learn, continuously, hidden patterns in the data. However, this is not the way the human mind learns. A person does not require millions of data and multiple iterations to solve a particular problem, since all they need are some examples to solve it. In this context, techniques such as semi-supervised learning or semi-supervised learning are playing an important role nowadays. Within Machine Learning techniques, we can find several well-differentiated approaches (see Figure 1). The supervised algorithms deal with labeled data sets and their objective is to construct predictive models, either classification (estimating…

16
Aug
Anticipating Customer Churn through Levers of Retention
  • Rubén Granados
  • 2919 Views
  • 0 Comment
  • Big Data . Churn Prediction . Data Science . Fuga de Clientes . Machine Learning . Palancas de Retencion .

Identification and analysis of signs of customer churn Knowing which customers might not continue as such has been the objective of classic customer churn models, which final output consisted of the probability of loss associated with each customer over a specific temporary time range. However, once we know the customer is going to leave, we must ask ourselves: What can we do to keep them? And taking it even further: What can we do to keep the customer from even considering leaving? Preventive Measures for Customer Churn in the Banking Sector At Synergic Partners, we have carried out a project for one of the largest banking entities in Spain responding to these questions with a methodology based on the combination…

30
May
Improving Effectiveness at a Call Center with Machine Learning
  • Víctor Gonzalez
  • 3719 Views
  • 0 Comment
  • Algorithms . Big Data . Data Science . Machine Learning . Procesamiento de Lenguaje Natural .

Understanding our clients and giving the best response to meeting their needs in the shortest amount of time possible is key to improving satisfaction and engagement with the company. The problem begins when the number of clients is high and we are receiving hundreds or thousands of messages per day. In this situation, we have two problems to solve. First, we have to prioritize which messages we are going to respond first and, second, we have to understand what it is that they are saying. It is clear that some messages will be more important or more urgent than others and that it will not always be easy to prioritize. To make things easier, we can use Machine Learning techniques…

08
Nov
A Brief History of Machine Learning
  • Víctor Gonzalez
  • 4548 Views
  • 0 Comment
  • Big Data . ciencia de los datos . Data Science . Machine Learning .

As members of the Machine Learning community, it would be a good idea for us all to have an idea of the history of the sector we work in. Although we are currently living through an authentic boom in Machine Learning, this field has not always been so prolific, going through periods of high expectations and advances as well as “winters” of severe stagnation. Birth [1952 - 1956] 1950 — Alan Turing creates the “Turing Test” to determine whether or not a machine is truly intelligent. In order to pass the test, the machine must be capable of making a human believe that it is another human instead of a computer. 1952 — Arthur Samuel writes the first computer program…

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