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\nLogistic Regression from Scratch\n

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\nLogistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. Logistic regression makes a binary...\nLogistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. Logistic regression makes a binary classification prediction based on the sigmoid function with n input features. The β[i] are coefficients that can be determined from a stochastic gradient descent or other optimization on the training dataset. The desired classification is either a 0 or 1, so the probability calculated with the sigmoid function is rounded to the nearest value.

0:00 Logistic Function
1:48 Crescent Example
3:40 Number Classification
5:17 LR from Scratch
7:15 Train test split
8:41 Predict Function
11:11 Update Coefficients
18:52 Plot Loss Function
19:47 Test Evaluation
22:40 Any Number of Inputs
32:45 Easy or Hard Classification
33:55 Cluster Standard Deviation
35:15 ML for Engineers Course\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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36:10
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Grade
10th - Higher Ed
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Subjects
STEM
3 more...
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2021
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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standard deviation, values, data, classification, probability
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Additional Tags

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apmonitor, learn, machine learning, scratch, learning rate, round, input feature, random state, confusion matrix, logistic regression, logistic function, loss function, data points, measured values, predicted a1, times x1, test set, predict function, measured label, squared error, source code, pair plot, test values, beta values, predicted label, number classification, function, learning, predict, additional, label, predicted
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Classroom Considerations

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Best For: Explaining a topic
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