switch to basic browser
📂
📝
📟

🌲 / learning Programming Python Fundamentals Part 4 Lesson 14, Machine Learning Classification, Regression and Clustering

c File Name Size files dur q vq aq Vc Ac Fmt Res fps T Date
-001. Lesson 14 Overview Machine Learning Classification, Regression and Clustering en.srt11513srtsrt2023-09-06 20:29:56
-001. Lesson 14 Overview Machine Learning Classification, Regression and Clustering.mp464503795426.89886612081075125h264aacmov1280x72030mp42023-09-06 19:10:51
-002. Introduction to Machine Learning en.srt25632srtsrt2023-09-06 20:29:57
-002. Introduction to Machine Learning.mp459133130971.755102486353125h264aacmov1280x80030mp42023-09-06 19:10:58
-003. Case Study Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 en.srt11610srtsrt2023-09-06 20:29:58
-003. Case Study Classification with k-Nearest Neighbors and the Digits Dataset, Part 1.mp428788011460.382041500366125h264aacmov1280x80030mp42023-09-06 19:11:01
-004. k-Nearest Neighbors Algorithm en.srt4471srtsrt2023-09-06 20:29:59
-004. k-Nearest Neighbors Algorithm.mp49165379198.089433370236125h264aacmov1280x80030mp42023-09-06 19:11:03
-005. k-Nearest Neighbors Algorithm Hyperparameters and Hyperparameter Tuning en.srt3625srtsrt2023-09-06 20:30:00
-005. k-Nearest Neighbors Algorithm Hyperparameters and Hyperparameter Tuning.mp48879911144.660317491357125h264aacmov1280x80030mp42023-09-06 19:11:05
-006. Loading the Dataset en.srt2522srtsrt2023-09-06 20:30:01
-006. Loading the Dataset.mp45418011107.717007402267128h264aacmov1280x80030mp42023-09-06 19:11:07
-007. Loading the Dataset Displaying the Description en.srt5601srtsrt2023-09-06 20:30:02
-007. Loading the Dataset Displaying the Description.mp417502087235.26458595461125h264aacmov1280x80030mp42023-09-06 19:11:10
-008. Loading the Dataset Checking the Sample and Target Sizes en.srt5143srtsrt2023-09-06 20:30:04
-008. Loading the Dataset Checking the Sample and Target Sizes.mp413701297206.727256530396125h264aacmov1280x80030mp42023-09-06 19:11:12
-009. Loading the Dataset A Sample Digit Image en.srt3885srtsrt2023-09-06 20:30:04
-009. Loading the Dataset A Sample Digit Image.mp48012309140.318186456323125h264aacmov1280x80030mp42023-09-06 19:11:14
-010. Loading the Dataset Preparing the Data for Use with Scikit-Learn en.srt4369srtsrt2023-09-06 20:30:05
-010. Loading the Dataset Preparing the Data for Use with Scikit-Learn.mp411696358175.217778534400125h264aacmov1280x80030mp42023-09-06 19:11:16
-011. Visualizing the Data en.srt11046srtsrt2023-09-06 20:30:07
-011. Visualizing the Data.mp425595662425.598549481347125h264aacmov1280x80030mp42023-09-06 19:11:20
-012. Splitting the Data for Training and Testing en.srt11886srtsrt2023-09-06 20:30:07
-012. Splitting the Data for Training and Testing.mp425234840430.497959468335125h264aacmov1280x80030mp42023-09-06 19:11:23
-013. Creating the Model en.srt2934srtsrt2023-09-06 20:30:08
-013. Creating the Model.mp47767574124.296417499366125h264aacmov1280x80030mp42023-09-06 19:11:25
-014. Training the Model en.srt7346srtsrt2023-09-06 20:30:09
-014. Training the Model.mp417050758270.930431503370125h264aacmov1280x80030mp42023-09-06 19:11:28
-015. Predicting Digit Classes en.srt7164srtsrt2023-09-06 20:30:10
-015. Predicting Digit Classes.mp417332489291.317551475342125h264aacmov1280x80030mp42023-09-06 19:11:30
-016. Case Study Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 en.srt1270srtsrt2023-09-06 20:30:11
-016. Case Study Classification with k-Nearest Neighbors and the Digits Dataset, Part 2.mp4266938448.088005444309128h264aacmov1280x80030mp42023-09-06 19:11:31
-017. Metrics for Model Accuracy Estimator Method score en.srt1233srtsrt2023-09-06 20:30:12
-017. Metrics for Model Accuracy Estimator Method score.mp4396789882.918005382247128h264aacmov1280x80030mp42023-09-06 19:11:33
-018. Metrics for Model Accuracy Confusion Matrix en.srt9291srtsrt2023-09-06 20:30:12
-018. Metrics for Model Accuracy Confusion Matrix.mp420615846387.587483425292125h264aacmov1280x80030mp42023-09-06 19:11:36
-019. Metrics for Model Accuracy Classification Report en.srt6460srtsrt2023-09-06 20:30:13
-019. Metrics for Model Accuracy Classification Report.mp415653553264.126984474340125h264aacmov1280x80030mp42023-09-06 19:11:38
-020. Metrics for Model Accuracy Visualizing the Confusion Matrix en.srt8285srtsrt2023-09-06 20:30:14
-020. Metrics for Model Accuracy Visualizing the Confusion Matrix.mp417009129332.76517408275125h264aacmov1280x80030mp42023-09-06 19:11:40
-021. K-Fold Cross-Validation en.srt10055srtsrt2023-09-06 20:30:15
-021. K-Fold Cross-Validation.mp424912397422.254875471338125h264aacmov1280x80030mp42023-09-06 19:11:43
-022. Running Multiple Models to Find the Best One en.srt10092srtsrt2023-09-06 20:30:16
-022. Running Multiple Models to Find the Best One.mp430000152420.815238570436125h264aacmov1280x80030mp42023-09-06 19:11:47
-023. Hyperparameter Tuning en.srt8825srtsrt2023-09-06 20:30:17
-023. Hyperparameter Tuning.mp423813277322.989569589456125h264aacmov1280x80030mp42023-09-06 19:11:49
-024. Case Study Time Series and Simple Linear Regression en.srt4696srtsrt2023-09-06 20:30:18
-024. Case Study Time Series and Simple Linear Regression.mp413252345192.563107550417125h264aacmov1280x80030mp42023-09-06 19:11:51
-025. Loading the Average High Temperatures into a DataFrame en.srt5826srtsrt2023-09-06 20:30:19
-025. Loading the Average High Temperatures into a DataFrame.mp411793138227.625215414281125h264aacmov1280x80030mp42023-09-06 19:11:53
-026. Splitting the Data for Training and Testing en.srt9916srtsrt2023-09-06 20:30:20
-026. Splitting the Data for Training and Testing.mp420220197338.895238477343125h264aacmov1280x80030mp42023-09-06 19:11:56
-027. Training the Model en.srt6098srtsrt2023-09-06 20:30:21
-027. Training the Model.mp413630881238.492154457323125h264aacmov1280x80030mp42023-09-06 19:11:59
-028. Testing the Model en.srt2859srtsrt2023-09-06 20:30:22
-028. Testing the Model.mp46283723108.855011461326128h264aacmov1280x80030mp42023-09-06 19:12:00
-029. Predicting Future Temperatures and Estimating Past Temperatures en.srt3671srtsrt2023-09-06 20:30:23
-029. Predicting Future Temperatures and Estimating Past Temperatures.mp48688754133.12522388125h264aacmov1280x80030mp42023-09-06 19:12:02
-030. Visualizing the Dataset with the Regression Line en.srt8567srtsrt2023-09-06 20:30:24
-030. Visualizing the Dataset with the Regression Line.mp419721490329.18932479345125h264aacmov1280x80030mp42023-09-06 19:12:05
-031. OverfittingUnderfitting en.srt2826srtsrt2023-09-06 20:30:25
-031. OverfittingUnderfitting.mp47475130101.378005589454128h264aacmov1280x80030mp42023-09-06 19:12:06
-032. Case Study Multiple Linear Regression with the California Housing Dataset en.srt2552srtsrt2023-09-06 20:30:26
-032. Case Study Multiple Linear Regression with the California Housing Dataset.mp47999664102.888005622487128h264aacmov1280x80030mp42023-09-06 19:12:07
-033. Loading the Dataset en.srt9673srtsrt2023-09-06 20:30:27
-033. Loading the Dataset.mp425998805415.822948500366125h264aacmov1280x80030mp42023-09-06 19:12:10
-034. Exploring the Data with Pandas en.srt10299srtsrt2023-09-06 20:30:27
-034. Exploring the Data with Pandas.mp425536722414.058231493360125h264aacmov1280x80030mp42023-09-06 19:12:14
-035. Visualizing the Features en.srt21713srtsrt2023-09-06 20:30:28
-035. Visualizing the Features.mp464764212808.495601640507125h264aacmov1280x80030mp42023-09-06 19:12:21
-036. Splitting the Data for Training and Testing en.srt1951srtsrt2023-09-06 20:30:30
-036. Splitting the Data for Training and Testing.mp4615974282.756009595460128h264aacmov1280x80030mp42023-09-06 19:12:22
-037. Training the Model en.srt6751srtsrt2023-09-06 20:30:30
-037. Training the Model.mp416453900267.029478492359125h264aacmov1280x80030mp42023-09-06 19:12:25
-038. Testing the Model en.srt2438srtsrt2023-09-06 20:30:32
-038. Testing the Model.mp46961876100.078005556421128h264aacmov1280x80030mp42023-09-06 19:12:26
-039. Visualizing the Expected vs. Predicted Prices en.srt9462srtsrt2023-09-06 20:30:32
-039. Visualizing the Expected vs. Predicted Prices.mp427730559374.862948591458125h264aacmov1280x80030mp42023-09-06 19:12:30
-040. Regression Model Metrics en.srt5052srtsrt2023-09-06 20:30:33
-040. Regression Model Metrics.mp412732154199.45941510377125h264aacmov1280x80030mp42023-09-06 19:12:31
-041. Choosing the Best Model en.srt9184srtsrt2023-09-06 20:30:34
-041. Choosing the Best Model.mp425970556364.924807569435125h264aacmov1280x80030mp42023-09-06 19:12:35
-042. Case Study Unsupervised Machine Learning, Part 1--Dimensionality Reduction en.srt7937srtsrt2023-09-06 20:30:35
-042. Case Study Unsupervised Machine Learning, Part 1--Dimensionality Reduction.mp420770748319.669116519386125h264aacmov1280x80030mp42023-09-06 19:12:38
-043. Loading the Digits Dataset en.srt2029srtsrt2023-09-06 20:30:36
-043. Loading the Digits Dataset.mp4275000875.256009292157128h264aacmov1280x80030mp42023-09-06 19:12:39
-044. Creating a TSNE Estimator for Dimensionality Reduction en.srt5019srtsrt2023-09-06 20:30:37
-044. Creating a TSNE Estimator for Dimensionality Reduction.mp412544484198.832472504371125h264aacmov1280x80030mp42023-09-06 19:12:41
-045. Transforming the Digits Dataset's Features into Two Dimensions en.srt4273srtsrt2023-09-06 20:30:38
-045. Transforming the Digits Dataset's Features into Two Dimensions.mp47805242164.00254380247125h264aacmov1280x80030mp42023-09-06 19:12:43
-046. Visualizing the Reduced Data en.srt7606srtsrt2023-09-06 20:30:39
-046. Visualizing the Reduced Data.mp413728603306.898163357224125h264aacmov1280x80030mp42023-09-06 19:12:45
-047. Visualizing the Reduced Data with Different Colors for Each Digit en.srt8518srtsrt2023-09-06 20:30:40
-047. Visualizing the Reduced Data with Different Colors for Each Digit.mp416755237308.523537434301125h264aacmov1280x80030mp42023-09-06 19:12:47
-048. Visualizing the Reduced Data in 3D en.srt7768srtsrt2023-09-06 20:30:41
-048. Visualizing the Reduced Data in 3D.mp423337772307.919841606473125h264aacmov1280x80030mp42023-09-06 19:12:51
-049. Case Study Unsupervised Machine Learning, Part 2--k-Means Clustering en.srt4879srtsrt2023-09-06 20:30:41
-049. Case Study Unsupervised Machine Learning, Part 2--k-Means Clustering.mp413177462198.832472530396125h264aacmov1280x80030mp42023-09-06 19:12:53
-050. Loading the Iris Dataset en.srt5043srtsrt2023-09-06 20:30:42
-050. Loading the Iris Dataset.mp413158467189.126531556423125h264aacmov1280x80030mp42023-09-06 19:12:55
-051. Exploring the Iris Dataset Descriptive Statistics with Pandas en.srt10073srtsrt2023-09-06 20:30:43
-051. Exploring the Iris Dataset Descriptive Statistics with Pandas.mp422713369364.622948498364125h264aacmov1280x80030mp42023-09-06 19:12:58
-052. Visualizing the Dataset with a Seaborn pairplot en.srt13407srtsrt2023-09-06 20:30:44
-052. Visualizing the Dataset with a Seaborn pairplot.mp437448031532.990839562428125h264aacmov1280x80030mp42023-09-06 19:13:02
-053. Using a KMeans Estimator en.srt7824srtsrt2023-09-06 20:30:45
-053. Using a KMeans Estimator.mp420273398319.622676507374125h264aacmov1280x80030mp42023-09-06 19:13:05
-054. Dimensionality Reduction with Principal Component Analysis en.srt15308srtsrt2023-09-06 20:30:46
-054. Dimensionality Reduction with Principal Component Analysis.mp438545239621.621406496362125h264aacmov1280x80030mp42023-09-06 19:13:09
-055. Choosing the Best Clustering Estimator en.srt12200srtsrt2023-09-06 20:30:47
-055. Choosing the Best Clustering Estimator.mp425592083486.38839420287125h264aacmov1280x80030mp42023-09-06 19:13:12

control-panel

π
f69dc311a936 // 3.77 TiB free of 3.98 TiB