Supervised learning

Supervised-learning

Supervised learning is a fundamental approach in machine learning where algorithms are trained on labeled datasets. In this method, each training example consists of input features paired with the correct output label, allowing the model to learn the relationship between them. The goal is to enable the model to make accurate predictions on new, unseen data by generalizing from the training data. Supervised learning is widely used in various applications, including image classification, speech recognition, and predictive analytics, making it one of the most popular and essential techniques in the field of artificial intelligence and data science.

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As the name suggests, this type of learning algorithms needed a supervisor, while training for a particular task. Many of us apply some supervised learning algorithms unknowingly. Like It is used in…

Supervised Machine Learning

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Machine Learning is a way to teach a machine without explicitly programming for it. It learns from its past experience and gives us the desired output. Types of Learning: Supervised Learning…

Machine Learning : Supervised Learning

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Supervised Machine Learning , Types of Supervised Machine Learning and ML algorithms. Machine Learning | Data Science | Data Analysis | Data

A Brief Introduction to Supervised Learning

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Supervised learning is the most common subbranch of machine learning today. Typically, new machine learning practitioners will begin their journey with supervised learning algorithms. Therefore, the…

What Is Supervised Learning?

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Get to know the Applications and Problems of Supervised Learning Continue reading on Towards Data Science

Supervised vs Unsupervised Learning in 2 Minutes

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Supervised learning algorithms take a dataset and use its features to learn some relationship with a corresponding set of labels. This process is known as training and, once complete, we would hope…

Supervised Learning, But A Lot Better: Semi-Supervised Learning

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Supervised learning was the first type of learning explored in the field of artificial intelligence. Since its conception, countless algorithms — varying in complexity from the humble logistic…

Self-Supervised Learning

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Machine learning is broadly divided into supervised, unsupervised, semi-supervised, and reinforcement learning problems. Machine learning has enjoyed the majority of success by tackling supervised…

Supervised Learning: Logistic Regression from basics to expert

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The Supervised learning machine learning algorithms are done when our data is labeled. We have two types of supervised machine learning algorithms Classification is the method used to predict the…

Supervised Machine learning — Linear Regression

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In Supervised Machine Learning, the models are trained by providing data that is tagged with a correct label. There are several algorithms under supervised machine learning and one of them is Linear…

Building Blocks of Supervised Learning

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The goal of Supervised learning is to predict an output based on a number of inputs. In Supervised learning problems, both input and output variables are measurable. This family of techniques consist…...

Unsupervised vs. Supervised Learning

Towards AI

I just started my initial steps into data science and machine learning, and, got introduced to “Supervised Learning” techniques as “Classifiers (Decisiontreeclassifer from sklearn kit), and on the…