Machine Learning (Part 1)

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What is Machine Learning?

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Machine learning is a sub-field of artificial intelligence where computers provide predictions based on patterns learned from data without explicit programming.

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What is Deep Learning?

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Deep learning is a subfield of machine learning inspired by the human brain's structure, requiring large amounts of data for training, used for complex cognitive problems.

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What is Data Science?

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Data science involves extracting value and insights from data, which can include predictive models using machine learning or insights from dashboards or reports.

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What is Supervised Machine Learning?

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Supervised machine learning involves training a model on a labeled dataset, where the data includes both input and correct output, to predict outcomes based on input data.

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Examples of Supervised Machine Learning

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Examples include image classification (e.g., recognizing cats and dogs) and email spam detection.

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What is Regression in Supervised Learning?

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Regression involves predicting a continuous outcome within a range, such as house price prediction based on various features.

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What is Classification in Supervised Learning?

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Classification involves categorizing outcomes into two or more categories, such as spam detection or customer churn prediction.

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What is Unsupervised Machine Learning?

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Unsupervised learning involves training a model on data without labeled outputs to find patterns or groupings, such as customer segmentation.

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What is Reinforcement Learning?

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Reinforcement learning involves an agent learning to make decisions by taking actions to maximize cumulative reward, based on feedback from its actions.

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What is Linear Regression?

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Linear regression models the relationship between independent and dependent variables, fitting a linear equation to observed data.