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Machine Learning Fundamentals - 50 Key Concepts

Learn machine learning basics with 50 flashcards covering supervised/unsupervised learning, neural networks, and evaluation metrics.

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Regression

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A type of supervised learning used to predict continuous numerical values. Predicting the temperature or stock market prices.

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Supervised Learning

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Learning from labeled training data to predict outcomes for unseen data. Used for predicting house prices or identifying spam emails.

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Classification

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A type of supervised learning used to categorize data into discrete classes. Sorting images into 'cat' or 'dog' categories.

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Labeled Data

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Data that consists of a set of training examples, each paired with an output label. A dataset of emails marked as 'Spam' or 'Not Spam'.

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Linear Regression

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An algorithm that models the relationship between a dependent variable and one or more independent variables using a straight line. Estimating sales based on advertising spend.

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Logistic Regression

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A classification algorithm used to estimate the probability of a binary outcome. Determining if a loan applicant will default (Yes/No).

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Support Vector Machines (SVM)

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An algorithm that finds the hyperplane that best separates different classes in feature space. Used in image recognition and text categorization.

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Decision Trees

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A flowchart-like structure used for classification and regression based on feature splits. Deciding whether to play tennis based on weather conditions.

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Random Forest

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An ensemble learning method that constructs multiple decision trees and merges them for more accurate predictions. Predicting customer churn in telecommunications.

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Gradient Boosting

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An ensemble technique that builds models sequentially, each correcting the errors of the previous one. Winning Kaggle competitions and high-accuracy ranking systems.

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