Creating systems that learn from their own experience is the goal of Machine Learning. This discipline explores the construction of algorithms that are able to recognize complex patterns and make predictions from data.
Machine Learning is changing the world. Its impact ranges from anti-spam filters to cancer investigation. It has enabled major scientific advances, such as a detailed knowledge of the human genome. Many everyday technological tools, such as product recommendation systems or search engines, has been also improved by Machine Learning.
When working with large volumes of data, it is inefficient, or even infeasible, to program the rules that set the infinite combinations of input data and their possible outputs. Machine Learning systems do not need these explicit instructions. They use information provided as examples to generalize behaviors. It is an iterative process in which the system continuously checks its results and makes the corresponding adaptations. In other words, the system is constantly learning.
Benefits of Machine Learning
Machine Learning is being applied in different sectors: finance, health, marketing, scientific research, education, energy, etc. At 3.14, we are committed to developing tailor-made solutions that maximize the benefits of Machine Learning. Among these benefits, it is worth mentioning the following:
- Saving time and money: Machine Learning allows data scientists to automate the creation of analytics models, which supposes a significant reduction of time and costs.
- Handling large data sets: The volume and complexity of Big Data make Machine Learning solutions increasingly necessary. These solutions are also able to process data in real time.
- Exploiting new opportunities: Finding hidden patterns in data brings to light new opportunities.
- Anticipating needs: Thanks to predictive analytics, it is possible to turn reactive practices into proactive ones.
- Managing risk: Predictive models also provide a great competitive advantage when managing risks. The use of Machine Learning solutions help to make informed decisions and, therefore, reduce uncertainty.
Custom Machine Learning
Our systems are built to meet the specific needs of each client. Thus, we use different types of Machine Learning:
- Supervised learning: The system is trained. It receives labeled training data consisting of inputs and desired outputs. The goal is to learn a general rule that maps inputs to outputs.
- Unsupervised learning: The system receives inputs without labels. The goal is to find hidden structure or unknown patterns from this unlabeled data.
- Reinforcement learning: The system interacts with its environment. It receives feedback in the form of punishment or reward and it learns by trial and error.
We can also classify the type of learning according to the desired output:
- Classification: The system is trained with a data set divided into classes and it produces a model capable of identifying to which of these categories a new input belongs.
- Regression: It is used in supervised learning to estimate the relationship among variables when the outputs are continuous rather than discrete.
- Clustering: It is used in unsupervised learning to divide the input into groups that share the same characteristics. Unlike in classification, the groups are not known beforehand.
- Dimensionality reduction: Its goal is to simplify inputs and to reduce the number of random variables under consideration.
Our experience helps us select the type of algorithm most suitable for each project:
- Support Vector Machines (SVMs): This set of supervised learning algorithms is used for classification and regression. Given a set of training examples divided into two categories, a SVM algorithm builds a model capable of identifying to which of these two categories a new example belongs.
- Bayesian networks: This model represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG).
- Decision tree learning: This approach consists of mapping observations about an item to predict its target value.
- Association rule learning: This method focuses on discovering relevant relationships between variables in a dataset.
- Artificial Neural Networks: This paradigm, inspired by the way biological nervous system works, is normally used to model complex relationships between inputs and outputs.
- Deep learning: This approach manages to model high-level abstractions using multiple layers of nonlinear processing.
We evaluate which Machine Learning approach each project needs. For that, we take into account factors such as size, quality and nature of the data, training time or the desired accuracy level.