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Updated index.md (#22280)

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* Updated index.md

Added more resources to the article

* Updated index.md

* Added more resources to the article

Moved the lines out of the code block as requested
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Ritul Srivastava
2018-11-19 19:33:11 +05:30
committed by Tom
parent bd0b5fa208
commit a7616e7b23
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6
guide/english/machine-learning/linear-regression/index.md
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@ -37,6 +37,8 @@ def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_i
return [b, m] return [b, m]
gradient_descent_runner(wheat_and_bread, 1, 1, 0.01, 100) gradient_descent_runner(wheat_and_bread, 1, 1, 0.01, 100)
``` ```
Code example is from <a href='http://blog.floydhub.com/coding-the-history-of-deep-learning/' target='_blank' rel='nofollow'>this article</a>. It also explains gradient descent and other essential concepts for deep learning. Code example is from <a href='http://blog.floydhub.com/coding-the-history-of-deep-learning/' target='_blank' rel='nofollow'>this article</a>. It also explains gradient descent and other essential concepts for deep learning.
@ -66,3 +68,7 @@ print(model.intercept_)
from sklearn import metrics from sklearn import metrics
print(metrics.accuracy_score(y_test, y_pred_class)) print(metrics.accuracy_score(y_test, y_pred_class))
``` ```
You can refer to this article for deeper insight into regression
https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/

3
guide/english/machine-learning/logistic-regression/index.md
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@ -36,6 +36,9 @@ penalizing the learning algorithms are used.
Cost(hθ(x),y)=−log(hθ(x)) if y = 1 Cost(hθ(x),y)=−log(hθ(x)) if y = 1
Cost(hθ(x),y)=−log(1−hθ(x)) if y = 0 Cost(hθ(x),y)=−log(1−hθ(x)) if y = 0
Refer to this article for clearing your basics https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/
#### Predictions using logistic regression: #### Predictions using logistic regression:
Logistic regression models the probability of the default class (i.e. the first class). Logistic regression models the probability of the default class (i.e. the first class).
You can classify results given by: You can classify results given by: