Machine Learning is a field of artificial intelligence(AI) that enables a computer system to learn and improve from data, without being explicitly programmed to perform a specific task. Instead of following predefined instructions, machine learning algorithms identify patterns in the data and use these patterns to make predictions or decisions.

Basic principles of Machine Learning

1. Data: The starting point for machine learning is a set of data. This data can be text, images, sounds, numerical measurements and so on. They are used to train the algorithm to recognize patterns or establish relationships between different variables.
2. Learning algorithm:
   • A machine learning algorithm is a method or set of rules that learns from data. Algorithms can be classified into three broad categories:
     • Supervised learning: The algorithm is trained on a set of labeled training data (where each example of data is associated with a correct answer). The aim is to learn a function that maps inputs to outputs.
     • Unsupervised learning: The algorithm is trained on unlabeled data, and must discover the underlying structure of the data (such as clusters or patterns).
     • Reinforcement learning: The algorithm learns through trial and error by interacting with an environment. It receives rewards or punishments depending on its actions, helping it to improve over time.
3. Model: The model is the result of the learning process. It is a mathematical representation of the relationships learned from the data. This model can then be used to make predictions or decisions on new data.
4. Prediction/Classification: Once the model has been built, it can be used to predict results on new data (in the case of supervised learning) or to identify structures in new data (in the case of unsupervised learning).
5. Evaluation: Model performance is evaluated using specific measures (such as precision, recall, ROC curve, etc.), to determine how well the model generalizes to new data.

Examples of Machine Learning applications

• Image recognition: Machine learning algorithms are used to identify objects, faces or text in images.
• Natural language processing (NLP): text analysis for machine translation, email classification (spam or non-spam), virtual assistance (such as chatbots).
• Financial prediction: Use of models to predict stock market trends or assess credit risk.
• Personalization: Recommend products, films or music according to user preferences.
• Fraud detection: Identification of suspicious transactions in financial systems.

Machine learning enables machines to “better understand” data and improve their performance on a specific task using experience (data). It is a key technology in many modern fields, where data plays a crucial role.