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The fundamentals of machine learning encompass a set of concepts and techniques that allow computers to learn from data and make predictions or decisions without being explicitly programmed. Here are some important machine learning fundamentals:
Data: Machine learning is based on the use of data. This data can be structured, unstructured, numeric, or text-based. The quality and relevance of the data are critical to learning success.
Characteristics: Features are individual characteristics or attributes extracted from data to identify patterns and relationships. Selecting relevant features is an important step in creating accurate models.
Models: Models are algorithms or mathematical functions used to learn from the data. There are several types of models, such as linear regression, decision trees, artificial neural networks, and support vector machines.
Learning: Machine learning is about learning from the data and adapting the models to improve predictions or decisions. This learning process can be supervised, unsupervised, or reinforced.
Training and testing: models are trained by training with existing data and then evaluated with test data to assess their performance. This helps avoid overfitting and ensures that the model can generalize to new data.
Error minimization: the goal of machine learning is to minimize the error or discrepancy between predicted and actual results. There are several methods for minimizing error, such as using cost functions and optimization algorithms.
Prediction and Decision Making: After training, the model can be used to make predictions or decisions for new, unknown data. This can be used in various application areas such as image recognition, speech processing, recommendation systems, medical diagnosis, and more.
These fundamentals form the foundation of machine learning and are extended by more advanced concepts such as deep learning, neural networks, and natural language processing to tackle more complex tasks.