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Machine learning is a branch of artificial intelligence that allows computers to learn from experience and complete tasks without being explicitly programmed. Machine learning is based on the idea that computers can develop algorithms that recognize patterns and structures in data and learn from them.
The machine learning process typically consists of the following steps:
Data collection:
First, data is collected that is relevant to the task being solved. This data can come from a variety of sources, such as sensors, databases, or the Internet.
Data cleaning and preparation: the collected data is prepared to ensure that it is of high quality and appropriate for the model. This may include tasks such as removing erroneous data, normalizing values, or converting data to an appropriate format.
Feature extraction: in this step, relevant features are extracted from the prepared data. This step is important to reduce the dimensionality of the data and capture the relevant information that is important for learning.
Modeling: This is where a machine learning model is created and trained on the prepared data. There are different types of learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, which can be applied depending on the nature of the task and the available data.
Model training: the model is trained on the prepared data by applying it to the data and adjusting its internal parameters to match the patterns in the data. During the training experiment, the model optimizes its parameters to achieve the desired results.
Evaluation and fine-tuning: after training, the model is evaluated on test data to assess its performance. If the model does not produce the desired results, it can be adjusted and trained again to improve performance. This step can be performed iteratively until the desired level of performance is achieved.
Prediction or decision making: After the model has been trained and evaluated, it can be used to make predictions or decisions when exposed to new data that it did not see during training.
These are the basic steps of machine learning. However, depending on the specific task and the available data, additional steps or techniques may be required.