Machine Learning (ML), or Machine Learning in Portuguese, is a branch of Artificial Intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn to perform tasks without being explicitly programmed to do so. This area of study and practice has expanded rapidly due to increased data availability and advances in computational power.
The central objective of Machine Learning is to create systems that can learn from past experiences, that is, from data. These systems can be trained to recognize patterns, make decisions and make predictions. Learning is achieved through the adaptation of mathematical models that are adjusted according to the input of new data, thus improving their performance in specific tasks.
The application of Machine Learning is vast and permeates several areas, from speech and image recognition to the personalization of user experiences on digital platforms, including advanced medical diagnoses and recommendation systems. With the growing amount of data generated daily, ML becomes an indispensable tool for analyzing and interpreting this information.
Types of Machine Learning
There are different types of learning in Machine Learning, each with its specific characteristics and methods. The most common are:
- Supervised Learning: In this type, the model is trained on a labeled data set, that is, each training example is associated with a correct answer. The algorithm learns to map inputs to outputs based on these examples and can make predictions or decisions for new, previously unseen data.
- Unsupervised Learning: Here, the model works with unlabeled data. The goal is to find hidden structures in the data, such as common clusters or patterns. Techniques such as clustering and dimensionality reduction are common in this category.
- Reinforcement Learning: In this scenario, the algorithm learns to make decisions through trial and error. He is rewarded or punished for his actions, and over time, he learns to optimize his behavior to maximize the reward. It is widely used in gaming, robotics and navigation.
Machine Learning Process
The Machine Learning process generally follows a series of steps:
- Data Collection: The first step involves acquiring data relevant to the problem you want to solve. This data can come from different sources and in different formats.
- Data Preprocessing: The collected data needs to be clean and organized. This may involve removing duplicate or irrelevant data, handling missing values, normalizing and transforming data.
- Data Division: The data is divided into training, validation and test sets. The training set is used to tune the model, the validation set to tune the hyperparameters and the test set to evaluate the model's performance.
- Model Choice: An appropriate Machine Learning model is selected for the problem. This can be a regression model, classification, clustering, among others.
- Model Training: The model is trained using the training dataset. During this process, the model adjusts its internal parameters to learn the relationship between input data and expected outputs.
- Model Evaluation: After training, the model is evaluated using the test dataset to check its ability to generalize to new data.
- Optimization and Tuning: Based on the evaluation results, the model can be tuned and optimized to improve its performance. This may involve changing hyperparameters or using regularization techniques.
- Implementation: Once optimized and validateded, the model is ready to be deployed in a production environment, where it can make predictions or make decisions in real time.
Understanding the foundations of Machine Learning is essential to enter the field of Artificial Intelligence and explore its possibilities. As technology advances, the ability to create and implement efficient ML models becomes increasingly valuable across industries and sectors of society.
