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00:53 Registration - Cool Farm Tool Registration - Cool Farm Tool	Registration - Cool Farm Tool This video is from the Cool Farm Toool the royal (dick) school of veterinary studies cpd cool farm tool account register tool inbox folder spam end policies dot app steps process registration videos introduction firm From Louise Connelly on April 21st, 2021 0 likes 45 plays Publisher Digital Education Unit Licence Type All rights reserved The University of Edinburgh Language English Date Created April 21st, 2021
30:07 IDS - Week 09 - 04 - Feature engineering IDS - Week 09 - 04 - Feature engineering	IDS - Week 09 - 04 - Feature engineering Feature engineering with recipes and fitting models with workflows data science rstats variables spam model recipe training variable testing emails step steps models workflow thing things month week function probability engineering outcome From Mine Cetinkaya-Rundel on November 16th, 2020 0 likes 18 plays 0 Course Code MATH08077 Licence Type Creative Commons - Attribution Non Commercial Share A Like
28:22 IDS - Week 09 - 03 - Prediction and overfitting IDS - Week 09 - 03 - Prediction and overfitting	IDS - Week 09 - 03 - Prediction and overfitting Making predictions based on models and splitting data into training and testing sets to avoid overfitting data science rstats spam model probability curve training variable testing variables models observations emails things function dataset truth rate fact level probabilities word From Mine Cetinkaya-Rundel on November 16th, 2020 0 likes 29 plays 0 Course Code MATH08077 Licence Type Creative Commons - Attribution Non Commercial Share A Like
19:28 IDS - Week 09 - 02 - Logistic regression IDS - Week 09 - 02 - Logistic regression	IDS - Week 09 - 02 - Logistic regression Predicting binary outcomes with logistic regression data science rstats spam model characters probability linear emails function logit regression minus success outcome specificity sensitivity infinity bit rate inbox slope models From Mine Cetinkaya-Rundel on November 16th, 2020 0 likes 26 plays 0 Course Code MATH08077 Licence Type Creative Commons - Attribution Non Commercial Share A Like
09:48 ML5: K-Nearest Neighbours Classification ML5: K-Nearest Neighbours Classification	ML5: K-Nearest Neighbours Classification In this video, we introduce the simplest classifier: the K-Nearest Neighbours classifier. This classifier has no trainable parameters, and classifies test points by finding its closest training… test point training neighbors class points classifier validation classification parameters hyperparameters vector dog george distance spam cat jackson vote step From Elliot Crowley on November 9th, 2020 0 likes 13 plays 0
06:48 ML4: An introduction to Classification ML4: An introduction to Classification	ML4: An introduction to Classification Here, we explain what is meant by a (binary) classifier. It is a black box that takes in some data and predicts whether it belongs to one of two possible classes. We also describe how we can split… classifier spam dog training test machine cat cats dogs examples algorithms emails classes things accuracy dataset algorithm reason musk elon From Elliot Crowley on November 5th, 2020 0 likes 9 plays 0
13:34 Receiver Operating Characteristic (ROC) Curve Receiver Operating Characteristic (ROC) Curve	Receiver Operating Characteristic (ROC) Curve Receiver Operating Characteristic (ROC) Curve iaml evaluation threshold rate positives classifier spam curve negatives point thresholds probability question systems left side bayes emails numbers rates function confidence From Nigel Goddard on September 21st, 2016 0 likes 2,693 plays 0 Licence Type Creative Commons - Attribution
01:35 Classification Error and Accuracy Classification Error and Accuracy	Classification Error and Accuracy Classification Error and Accuracy iaml evaluation accuracy error negatives instances errors classification events class measure spam testing rate earthquake users engine earthquakes cases classes problem things From Nigel Goddard on September 21st, 2016 0 likes 2,202 plays 0 Licence Type Creative Commons - Attribution
03:10 False Positives and False Negatives False Positives and False Negatives	False Positives and False Negatives False Positives and False Negatives iaml evaluation positives negatives diagonal spam blocks classification numbers building columns rows classes matrix confusion humans truth things emails row measures bunch From Nigel Goddard on September 21st, 2016 0 likes 2,148 plays 0 Licence Type Creative Commons - Attribution
04:43 Generalization Error Generalization Error	Generalization Error Generalization Error generalization error systems science predict philosophical terminology integral cybernetics general form measure bitmaps tells caption complete graphics file formats human communication classification training error system label observations scientific method evaluation function iaml probability effects regression difference probabilistic real numbers compute equation value political philosophy future values truth logic finite number distribution predictor error training generalization sum attribute step range tomorrow spam From Nigel Goddard on September 21st, 2016 0 likes 2,868 plays 0 Licence Type Creative Commons - Attribution
05:49 Naive Bayes for Spam Detection Naive Bayes for Spam Detection	Naive Bayes for Spam Detection Naive Bayes for Spam Detection probability predict attribute labels number computer happen occur caption complete review presentation email independence iaml separate naive user thought chance contradictory effects classify password word password times thought thing advertising representation classifier spam word class words bayes emails minus things probabilities nonspam lots classifiers training numbers From Nigel Goddard on September 17th, 2016 2 likes 2,780 plays 1 Licence Type Creative Commons - Attribution
03:04 The Zero-Frequency Problem The Zero-Frequency Problem	The Zero-Frequency Problem The Zero-Frequency Problem iaml words spam epsilon emails word probabilities probability bayes opportunity investment stuff law text epsilons half statistics times thing fact account From Nigel Goddard on September 17th, 2016 1 likes 2,626 plays 0 Licence Type Creative Commons - Attribution
05:32 Bayesian Classification Bayesian Classification	Bayesian Classification Bayesian Classification probability predict problematic computer mathematical terminology classification interesting percent predictions class assuming scientific method email classify function iaml digit predicting stick ontology small number classifiers compute classified probabilistic times values representation quantity pixel effects spam classes classifier words probabilities bayes x. model classifications y. From Nigel Goddard on September 17th, 2016 1 likes 3,565 plays 0 Licence Type Creative Commons - Attribution
05:16 Supervised vs Unsupervised Learning Supervised vs Unsupervised Learning	Supervised vs Unsupervised Learning Supervised vs Unsupervised Learning iaml data algorithm learning method spam evaluation algorithms output emails training thing tomorrow lots methods task quantity instances structure cases accuracy labels From Nigel Goddard on September 17th, 2016 1 likes 2,184 plays 0 Licence Type Creative Commons - Attribution
05:25 Representing Text with Categorical Attributes Representing Text with Categorical Attributes	Representing Text with Categorical Attributes Representing Text with Categorical Attributes iaml data words representation word algorithm thing values attributes spam change attribute learning text problem point emails war vector investment meaning isolation From Nigel Goddard on September 17th, 2016 1 likes 2,140 plays 0 Licence Type Creative Commons - Attribution
04:39 Joint Distributions Joint Distributions	Joint Distributions Joint Distributions iaml maths probability word password spam probabilities ham table sum frequency variables x. span y. mails emails thing kind variable purpose columns From Nigel Goddard on September 8th, 2016 0 likes 2,147 plays 0 Licence Type Creative Commons - Attribution

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