Machine Learning

Machine Learning is a discipline within artificial intelligence that allows machines to learn from data and improve their performance over time, without being explicitly programmed for each task. Unlike conventional software, which follows direct instructions and fixed rules to execute functions, ML uses algorithms that adapt and adjust their responses based on patterns and relationships discovered in the data.

Machine Learning and conventional software can handle structured data, but the way each uses this data is fundamentally different. ML systems are not constrained by a fixed set of instructions. Instead, they learn from large data sets, training themselves to recognize patterns and base their predictions and decisions on them. This training is crucial, as it enables ML systems to achieve high levels of accuracy in complex tasks such as classification, prediction and pattern recognition.

This interactive learning process enables ML systems to continuously improve their performance. Unlike traditional software, which performs actions based solely on preprogrammed instructions, ML systems adapt and evolve autonomously, optimizing their behavior as they process more data and learn from new experiences.

The effective implementation of ML requires a different profile from those found in conventional information systems. Data scientists. They are experts in statistics, mathematics, modeling and variable engineering. Their mission is to design and optimize algorithms that can extract actionable information from large volumes of data, structured or not.

These mining, prediction, and classification properties find their use in all kinds of business situations, to name the most common:

• Data exploration, identification of hidden patterns and dependencies…
• Demand forecasting , inventory planning…
• Optimization of prices, processes, routes, resources, machines…
• Recommendation of products, services, components, suppliers…
• Task automation : classification, diagnostics…
• Detection of fraud, failures, incidents, abandonment…

These models are of interest in all areas of companies:

• Commercial areas (Marketing, Sales, Product, Research): 360º customer analysis, hyper-segmentation, online product recommendation, churn forecasting, dynamic price optimization…

• Operations areas (Manufacturing, Quality, Supply Chain, Logistics): predictive maintenance (Manufacturing, Quality, Supply Chain, Logistics): predictive maintenance, machine and process optimization, early failure detection, quality improvements…

• Corporate areas (IT, HR, Finance, Administration): ticket and document classification, cash forecasting, absenteeism…

Each type of Machine Learning has its specific methodologies and applications:

• Supervised Learning (e.g., credit risk, diagnostic automation): involves training a model on a set of labeled data, where the correct answers (or outputs) are already provided. The model makes predictions and adjusts based on the accuracy of these predictions compared to the actual labels. It is widely used for classification and regression, such as predicting the probability of an event or determining continuous values, respectively.

• Unsupervised Learning (e.g., hypersegmentation): in this type, the data is unlabeled. The goal is to find inherent patterns or structures in the data. Common techniques include clustering, which groups similar data, and dimensionality reduction, which simplifies the data without losing significant features.

• Semi-Supervised Learning (e.g., recommender systems): combines elements of both supervised and unsupervised learning . It is used when a large set of unlabeled data and a small set of labeled data are available. The model is first trained on the labeled data and then predictions or patterns are refined on the larger unlabeled data set.

• Self-Supervised Learning (e.g., social media content optimization): is a variant of unsupervised learning, where the data generates its own labels from contextual information in the data. A common example is the use of pretext techniques to generate tasks, such as predicting the next word in a sequence, where the model is trained to learn useful representations of the data.

We present here a list of fundamental machine learning techniques that data scientists apply to interpret data and solve specific problems.

• Regression: predicts continuous values (e.g., Linear Regression, Polynomial Regression).
• Classification: predicts discrete categories or labels (e.g. Logistic Regression, Decision Trees, Random Forest, Support Vector Machines SVM, K-Nearest Neighbors)
• Clustering: grouping data into clusters containing similar characteristics (e.g. K-Means, Hierarchical Clustering, DBSCAN).
• Anomaly Detection: algorithms for the identification of unusual patterns that do not conform to expected behavior.
• Association: identifies rules that describe large portions of data (for example: Apriori, Eclat)
• Dimensionality Reduction: reduces the number of variables under consideration (e.g. Principal Component Analysis, t-Distributed Stochastic Neighbors Embedding).
• Reinforcement Learning: algorithms that create a model of the environment (e.g. Markov Decision Processes), or learn policies directly (e.g. Q-learning, Policy Gradients).
• Ensemble methods: combine predictions from several models to improve accuracy (e.g. Bagging, Boosting, Stacking).
• Neural Networks and Deep Learning: uses artificial neurons with multiple layers to discover patterns in data (e.g. Convolutional Neural Networks for imaging tasks, Recurrent Neural Networks for sequential data).
• Transfer Learning: reuses a pre-trained model in a new problem, adapting it to new tasks.
• Cross Validation Techniques: methods to evaluate the performance of a model (e.g. k-fold cross validation).
• Engineering and Feature Selection: creation of new variables based on existing ones, and selecting the most important ones to improve model performance