Machine Learning - Stanford

1. Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe 69.61 MB
2. 01.4-V2-Introduction-UnsupervisedLearning.mp4 38.56 MB
3. 02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4 31.62 MB
4. 01.2-V2-Introduction-WhatIsMachineLearning.mp4 30.40 MB
5. 02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4 27.00 MB
6. 12.6-SupportVectorMachines-UsingAnSVM.mp4 25.76 MB
7. 02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4 25.47 MB
8. 05.2-OctaveTutorial-MovingDataAround.mp4 25.42 MB
9. 03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4 24.60 MB
10. 12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4 22.91 MB
11. 03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp4 22.35 MB
12. 06.6-LogisticRegression-AdvancedOptimization.mp4 21.59 MB
13. 09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4 21.25 MB
14. 05.1-OctaveTutorial-BasicOperations.mp4 20.69 MB
15. 03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp4 20.23 MB
16. 05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp4 19.69 MB
17. 15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp4 19.40 MB
18. 02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4 18.92 MB
19. 12.4-SupportVectorMachines-KernelsI.mp4 18.75 MB
20. 17.2-RecommenderSystems-ContentBasedRecommendations.mp4 18.73 MB
21. 12.5-SupportVectorMachines-KernelsII.mp4 18.31 MB
22. 09.7-NeuralNetworksLearning-PuttingItTogether.mp4 17.88 MB
23. 12.1-SupportVectorMachines-OptimizationObjective.mp4 17.77 MB
24. 16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp4 17.75 MB
25. 15.1-DimensionalityReduction-MotivationIDataCompression.mp4 17.63 MB
26. 06.3-LogisticRegression-DecisionBoundary.mp4 17.51 MB
27. 18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4 17.30 MB
28. 11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4 17.29 MB
29. 16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4 17.27 MB
30. 09.3-NeuralNetworksLearning-BackpropagationIntuition.mp4 17.14 MB
31. 11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4 16.94 MB
32. 16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4 16.92 MB
33. 08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4 16.84 MB
34. 05.6-OctaveTutorial-Vectorization.mp4 16.83 MB
35. 18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4 16.40 MB
36. 02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4 16.30 MB
37. 10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4 16.13 MB
38. 18.5-LargeScaleMachineLearning-OnlineLearning.mp4 15.96 MB
39. 15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4 15.80 MB
40. 01.3-V2-Introduction-SupervisedLearning.mp4 15.45 MB
41. 16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp4 15.43 MB
42. 16.3-AnomalyDetection-Algorithm.mp4 15.30 MB
43. 09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp4 15.07 MB
44. 09.4-NeuralNetworksLearning-GradientChecking.mp4 14.76 MB
45. 14.2-Clustering-KMeansAlgorithm.mp4 14.67 MB
46. 08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4 14.41 MB
47. 18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4 14.41 MB
48. 08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4 14.37 MB
49. 11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4 14.24 MB
50. 06.4-LogisticRegression-CostFunction.mp4 14.11 MB