Machine learning is a branch of artificial intelligence (AI) that deals with the development of algorithms and models. They enable computers to learn from data and experience, and then use this knowledge to solve specific tasks without explicit programming. The main goal of machine learning is to enable computers to identify patterns and relationships in data. This makes it a very important tool for solving problems that would be difficult or impossible to solve using traditional programming.
There are different types of machine learning, including:
- Learning with a teacher (Supervised Learning) : The model is trained based on pairs of input-data and output-data (labeled data). The goal is to teach the model to predict output values for new, unlabeled data.
- Unsupervised Learning : The model learns from unlabeled data and aims to discover structure or patterns in the data. This includes clustering and dimensionality reduction.
- Semi-Supervised Learning : Combines elements of supervised learning and unsupervised learning, where the model is trained on a small amount of labeled data and a large amount of unlabeled data.
- Reinforcement Learning : The model learns to make decisions based on interaction with the environment and receives a reward or punishment based on its decisions. The goal is to teach the model how to optimize its decisions to maximize reward.
Machine learning steps
- Data collection : The first step is to obtain or generate data on which the model will be trained. Data can come from a variety of sources, such as measurements, text documents, images, or other forms of data.
- Data preprocessing : Data often requires editing and cleaning to make it suitable for training a model. This may include removing missing values, normalizing and scaling, and transforming the data into an appropriate form.
- Model selection and training : The model is selected based on the nature of the task and the type of data. Then the model is trained on the data, meaning it is presented with data to learn from and the model tries to learn the patterns and relationships in the data.
- Model evaluation : After training, the model is evaluated using test data that are not part of the training data. This evaluation makes it possible to assess how well the model works and whether it achieves the expected results.
- Optimizing and Tuning the Model : If the performance of the model is insufficient, you can optimize the model in various ways. This includes hyperparameter adjustments, model architecture changes, or data preprocessing improvements.
- Prediction and deployment : After successful training and evaluation, the model can be used to predict outputs for new input data. This can be in the form of automatic text evaluation, image classification, product recommendation and other tasks.
Machine learning is a dynamic field with the constant development of new techniques and methods. In addition, there are a number of tools and frameworks that allow engineers and researchers to work with machine learning. This makes the process accessible even to those without a deep understanding of machine learning.