Machine Learning is a branch of artificial intelligence that enables computer systems to learn from data, identify patterns, and make decisions with minimal human intervention. Instead of being explicitly programmed for each task, these systems improve their performance as they are exposed to new data.
The primary goal of Machine Learning is to create models capable of generalizing from past examples to make predictions or classifications on new data. It is the foundation of many modern applications, from product recommendations to medical diagnostics.
Supervised learning is the most common type of Machine Learning. It uses “labeled” data, meaning data where each input is associated with the corresponding correct output. The model learns to map inputs to outputs based on these pairs.
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Unlike supervised learning, unsupervised learning works with “unlabeled” data. The model must find hidden structures, patterns, or groupings in the data by itself, without any prior indication of the correct answers.
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Reinforcement learning involves an “agent” that learns to make decisions by interacting with an environment. The agent receives “rewards” for good actions and “penalties” for bad ones, and its goal is to maximize the cumulative reward over time.
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Deep Learning is a subfield of Machine Learning that is inspired by the structure and function of the human brain, using artificial neural networks with many layers (hence the term “deep”). These networks are capable of learning complex and hierarchical representations of data.
While traditional Machine Learning often requires manual feature extraction from data, Deep Learning excels at learning these features directly from raw data, making it particularly powerful for tasks such as image recognition, natural language understanding, and content generation.
For a model to learn effectively, it needs a training dataset. Once trained, its performance is evaluated on a separate test dataset, which the model has never seen before. This verifies its ability to generalize and make accurate predictions on new data.
A model is the mathematical representation learned from data (e.g., a function, a neural network). An algorithm is the procedure or set of rules used to build and train this model from data (e.g., linear regression, decision trees, neural networks).
The cost (or loss) function measures the error between the model’s predictions and the actual values. The goal of training is to minimize this cost function, meaning the model makes increasingly accurate predictions.
Optimization is the process of adjusting the model’s parameters to minimize the cost function. Optimization algorithms (like gradient descent) guide the model to find the best parameters that reduce prediction error.
Overfitting occurs when the model learns the training data too well, including noise and specificities, making it unable to generalize to new data. It “memorizes” rather than “learns.”
Underfitting occurs when the model is too simple to capture the underlying relationships in the training data. It fails to learn enough from the data and performs poorly on both training and test data.
