[FreeTutorials.Us] Udemy - machine-learning-course-with-python

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[FreeTutorials.Us] Udemy - machine-learning-course-with-python

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种子哈希：03705fe20e1d0bca7e1938d98089710ec067e809
文件大小： 2.94G
已经下载：996次
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收录时间：2018-02-09
最近下载：2024-11-13

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文件列表

  • 02 Getting Started with Anaconda/attached_files/008 Navigating the Spyder Jupyter Notebook Interface/0204.zip 43.3 kB
  • 02 Getting Started with Anaconda/attached_files/009 Downloading the IRIS Datasets/0205.zip 234.1 kB
  • 02 Getting Started with Anaconda/attached_files/010 Data Exploration and Analysis/0206.zip 45.8 kB
  • 02 Getting Started with Anaconda/attached_files/011 Presenting Your Data/0207.zip 1.1 MB
  • 03 Regression/attached_files/014 Working with Scikit-Learn/0303.zip 933.8 kB
  • 03 Regression/attached_files/015 Boston Housing Data - EDA/0304.zip 2.0 MB
  • 03 Regression/attached_files/016 Correlation Analysis and Feature Selection/0305.zip 2.2 MB
  • 03 Regression/attached_files/017 Simple Linear Regression Modelling with Boston Housing Data/0306.zip 2.7 MB
  • 03 Regression/attached_files/018 Robust Regression/0307.zip 2.9 MB
  • 03 Regression/attached_files/019 Evaluate Model Performance/0308.zip 3.1 MB
  • 03 Regression/attached_files/020 Multiple Regression with statsmodel/0309.zip 2.7 MB
  • 03 Regression/attached_files/021 Multiple Regression and Feature Importance/0310.zip 2.8 MB
  • 03 Regression/attached_files/022 Ordinary Least Square Regression and Gradient Descent/0311.zip 144.8 kB
  • 03 Regression/attached_files/023 Regularised Method for Regression/0312.zip 264.9 kB
  • 03 Regression/attached_files/024 Polynomial Regression/0313.zip 383.9 kB
  • 03 Regression/attached_files/025 Dealing with Non-linear relationships/0314.zip 185.1 kB
  • 03 Regression/attached_files/026 Feature Importance Revisited/0315.zip 231.3 kB
  • 03 Regression/attached_files/027 Data Pre-Processing 1/0316.zip 159.6 kB
  • 03 Regression/attached_files/028 Data Pre-Processing 2/0317.zip 160.7 kB
  • 03 Regression/attached_files/029 Variance Bias Trade Off - Validation Curve/0318.zip 540.8 kB
  • 03 Regression/attached_files/030 Variance Bias Trade Off - Learning Curve/0319.zip 733.7 kB
  • 03 Regression/attached_files/031 Cross Validation/0320.zip 97.0 kB
  • 04 Classification/attached_files/033 Logistic Regression 1/0403.zip 19.9 kB
  • 04 Classification/attached_files/034 Logistic Regression 2/0404.zip 20.5 kB
  • 04 Classification/attached_files/035 MNIST Project 1 - Introduction/0405.zip 579.4 kB
  • 04 Classification/attached_files/036 MNIST Project 2 - SGDClassifier/0406.zip 579.4 kB
  • 04 Classification/attached_files/037 MNIST Project 3 - Performance Measures/0407.zip 579.8 kB
  • 04 Classification/attached_files/038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score/0408.zip 587.7 kB
  • 04 Classification/attached_files/039 MNIST Project 5 - Precision and Recall Tradeoff/0409.zip 645.3 kB
  • 04 Classification/attached_files/040 MNIST Project 6 - The ROC Curve/0410.zip 700.6 kB
  • 05 Support Vector Machine SVM/attached_files/043 Support Vector Machine SVM Concepts/0502.zip 223.8 kB
  • 05 Support Vector Machine SVM/attached_files/044 Linear SVM Classification/0503.zip 457.4 kB
  • 05 Support Vector Machine SVM/attached_files/045 Polynomial Kernel/0504.zip 502.9 kB
  • 05 Support Vector Machine SVM/attached_files/046 Gaussian Radial Basis Function/0505.zip 546.3 kB
  • 05 Support Vector Machine SVM/attached_files/047 Support Vector Regression/0506.zip 586.8 kB
  • 06 Tree/attached_files/050 What is Decision Tree/0602.zip 116.0 kB
  • 06 Tree/attached_files/051 Training a Decision Tree/0603.zip 116.4 kB
  • 06 Tree/attached_files/052 Visualising a Decision Trees/0604.zip 162.5 kB
  • 06 Tree/attached_files/053 Decision Tree Learning Algorithm/0605.zip 234.8 kB
  • 06 Tree/attached_files/054 Decision Tree Regression/0606.zip 284.6 kB
  • 06 Tree/attached_files/055 Overfitting and Grid Search/0607.zip 305.1 kB
  • 06 Tree/attached_files/056 Where to From Here/0608.zip 317.2 kB
  • 06 Tree/attached_files/057 Project HR - Loading and preprocesing data/0609.zip 64.9 kB
  • 06 Tree/attached_files/058 Project HR - Modelling/0610.zip 65.6 kB
  • 07 Ensemble Machine Learning/attached_files/060 Ensemble Learning Methods Introduction/0702.zip 2.3 kB
  • 07 Ensemble Machine Learning/attached_files/061 Bagging Part 1/0703.zip 7.8 kB
  • 07 Ensemble Machine Learning/attached_files/062 Bagging Part 2/0704.zip 8.3 kB
  • 07 Ensemble Machine Learning/attached_files/063 Random Forests/0705.zip 9.0 kB
  • 07 Ensemble Machine Learning/attached_files/064 Extra-Trees/0706.zip 9.3 kB
  • 07 Ensemble Machine Learning/attached_files/065 AdaBoost/0707.zip 9.0 kB
  • 07 Ensemble Machine Learning/attached_files/066 Gradient Boosting Machine/0708.zip 61.9 kB
  • 07 Ensemble Machine Learning/attached_files/067 XGBoost/0709.zip 3.0 kB
  • 07 Ensemble Machine Learning/attached_files/068 Project HR - Human Resources Analytics/0710.zip 129.8 kB
  • 07 Ensemble Machine Learning/attached_files/069 Ensemble of ensembles Part 1/0711.zip 3.7 kB
  • 07 Ensemble Machine Learning/attached_files/070 Ensemble of ensembles Part 2/0712.zip 5.5 kB
  • 08 k-Nearest Neighbours kNN/attached_files/072 kNN Concepts/0802.zip 1.6 kB
  • 08 k-Nearest Neighbours kNN/attached_files/073 kNN and Iris Dataset Demo/0803.zip 26.8 kB
  • 08 k-Nearest Neighbours kNN/attached_files/075 Project Cancer Detection Part 1/0805.zip 41.7 kB
  • 08 k-Nearest Neighbours kNN/attached_files/076 Project Cancer Detection Part 2/0806.zip 41.7 kB
  • 09 Dimensionality Reduction/attached_files/078 Dimensionality Reduction Concept/0902.zip 2.1 kB
  • 09 Dimensionality Reduction/attached_files/079 PCA Introduction/0903.zip 89.4 kB
  • 09 Dimensionality Reduction/attached_files/082 Project Abalone/09-Project-Abalone.zip 16.6 kB
  • 09 Dimensionality Reduction/attached_files/083 Project Wine 2 Choosing the Number of Components/0906.zip 108.6 kB
  • 09 Dimensionality Reduction/attached_files/084 Kernel PCA/0907.zip 110.8 kB
  • 09 Dimensionality Reduction/attached_files/085 Kernel PCA Demo/0908.zip 110.8 kB
  • 09 Dimensionality Reduction/attached_files/086 LDA Comparison between LDA and PCA/0909.zip 79.0 kB
  • 10 Unsupervised Learning Clustering/attached_files/088 Clustering Concepts/1002.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/089 MLextend/1003.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/090 Wards Agglomerative Hierarchical Clustering/1004.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/091 Truncating Dendrogram/1005.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/092 k-Means Clustering/1006.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/093 Elbow Method/1007.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/094 Silhouette Analysis/1008.zip 450.2 kB
  • 10 Unsupervised Learning Clustering/attached_files/095 Mean Shift/1009.zip 450.2 kB
  • 02 Getting Started with Anaconda/quizzes/001 Getting Started.html 11.1 kB
  • 03 Regression/quizzes/002 Machine Learning Basic Concepts.html 9.2 kB
  • 03 Regression/quizzes/003 Section 3.html 11.7 kB
  • 01 Introduction/001 What Does the Course Cover.mp4 10.4 MB
  • 01 Introduction/002 How to Succeed in This Course.html 2.1 kB
  • 01 Introduction/003 Project Files.html 1.9 kB
  • 02 Getting Started with Anaconda/004 Windows OS Downloading Installing Anaconda.mp4 67.5 MB
  • 02 Getting Started with Anaconda/005 Windows OS Managing Environment.mp4 19.7 MB
  • 02 Getting Started with Anaconda/006 Mac OS Intructions on Installing Anaconda and Managing Environment.html 516 Bytes
  • 02 Getting Started with Anaconda/007 Practice Activity Create a New Environment.html 737 Bytes
  • 02 Getting Started with Anaconda/008 Navigating the Spyder Jupyter Notebook Interface.mp4 43.4 MB
  • 02 Getting Started with Anaconda/009 Downloading the IRIS Datasets.mp4 11.0 MB
  • 02 Getting Started with Anaconda/010 Data Exploration and Analysis.mp4 36.5 MB
  • 02 Getting Started with Anaconda/011 Presenting Your Data.mp4 48.8 MB
  • 03 Regression/012 Introduction.mp4 12.3 MB
  • 03 Regression/013 Categories of Machine Learning.mp4 23.4 MB
  • 03 Regression/014 Working with Scikit-Learn.mp4 48.2 MB
  • 03 Regression/015 Boston Housing Data - EDA.mp4 64.4 MB
  • 03 Regression/016 Correlation Analysis and Feature Selection.mp4 23.7 MB
  • 03 Regression/017 Simple Linear Regression Modelling with Boston Housing Data.mp4 35.4 MB
  • 03 Regression/018 Robust Regression.mp4 35.1 MB
  • 03 Regression/019 Evaluate Model Performance.mp4 62.0 MB
  • 03 Regression/020 Multiple Regression with statsmodel.mp4 62.5 MB
  • 03 Regression/021 Multiple Regression and Feature Importance.mp4 46.8 MB
  • 03 Regression/022 Ordinary Least Square Regression and Gradient Descent.mp4 52.5 MB
  • 03 Regression/023 Regularised Method for Regression.mp4 50.1 MB
  • 03 Regression/024 Polynomial Regression.mp4 45.9 MB
  • 03 Regression/025 Dealing with Non-linear relationships.mp4 29.8 MB
  • 03 Regression/026 Feature Importance Revisited.mp4 27.5 MB
  • 03 Regression/027 Data Pre-Processing 1.mp4 36.6 MB
  • 03 Regression/028 Data Pre-Processing 2.mp4 52.0 MB
  • 03 Regression/029 Variance Bias Trade Off - Validation Curve.mp4 46.0 MB
  • 03 Regression/030 Variance Bias Trade Off - Learning Curve.mp4 44.5 MB
  • 03 Regression/031 Cross Validation.mp4 46.9 MB
  • 04 Classification/032 Introduction.mp4 8.3 MB
  • 04 Classification/033 Logistic Regression 1.mp4 28.2 MB
  • 04 Classification/034 Logistic Regression 2.mp4 43.8 MB
  • 04 Classification/035 MNIST Project 1 - Introduction.mp4 36.2 MB
  • 04 Classification/036 MNIST Project 2 - SGDClassifier.mp4 26.7 MB
  • 04 Classification/037 MNIST Project 3 - Performance Measures.mp4 27.7 MB
  • 04 Classification/038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score.mp4 48.3 MB
  • 04 Classification/039 MNIST Project 5 - Precision and Recall Tradeoff.mp4 46.0 MB
  • 04 Classification/040 MNIST Project 6 - The ROC Curve.mp4 35.4 MB
  • 04 Classification/041 MNIST Exercise.html 826 Bytes
  • 05 Support Vector Machine SVM/042 Introduction.mp4 4.9 MB
  • 05 Support Vector Machine SVM/043 Support Vector Machine SVM Concepts.mp4 54.3 MB
  • 05 Support Vector Machine SVM/044 Linear SVM Classification.mp4 33.1 MB
  • 05 Support Vector Machine SVM/045 Polynomial Kernel.mp4 52.8 MB
  • 05 Support Vector Machine SVM/046 Gaussian Radial Basis Function.mp4 47.0 MB
  • 05 Support Vector Machine SVM/047 Support Vector Regression.mp4 18.2 MB
  • 05 Support Vector Machine SVM/048 Advantages and Disadvantages of SVM.mp4 13.7 MB
  • 06 Tree/049 Introduction.mp4 5.9 MB
  • 06 Tree/050 What is Decision Tree.mp4 35.7 MB
  • 06 Tree/051 Training a Decision Tree.mp4 17.4 MB
  • 06 Tree/052 Visualising a Decision Trees.mp4 57.9 MB
  • 06 Tree/053 Decision Tree Learning Algorithm.mp4 38.8 MB
  • 06 Tree/054 Decision Tree Regression.mp4 35.4 MB
  • 06 Tree/055 Overfitting and Grid Search.mp4 57.1 MB
  • 06 Tree/056 Where to From Here.mp4 12.2 MB
  • 06 Tree/057 Project HR - Loading and preprocesing data.mp4 59.5 MB
  • 06 Tree/058 Project HR - Modelling.mp4 17.3 MB
  • 07 Ensemble Machine Learning/059 Introduction.mp4 5.5 MB
  • 07 Ensemble Machine Learning/060 Ensemble Learning Methods Introduction.mp4 29.0 MB
  • 07 Ensemble Machine Learning/061 Bagging Part 1.mp4 58.2 MB
  • 07 Ensemble Machine Learning/062 Bagging Part 2.mp4 39.1 MB
  • 07 Ensemble Machine Learning/063 Random Forests.mp4 45.2 MB
  • 07 Ensemble Machine Learning/064 Extra-Trees.mp4 22.6 MB
  • 07 Ensemble Machine Learning/065 AdaBoost.mp4 41.8 MB
  • 07 Ensemble Machine Learning/066 Gradient Boosting Machine.mp4 47.0 MB
  • 07 Ensemble Machine Learning/067 XGBoost.mp4 53.8 MB
  • 07 Ensemble Machine Learning/068 Project HR - Human Resources Analytics.mp4 92.4 MB
  • 07 Ensemble Machine Learning/069 Ensemble of ensembles Part 1.mp4 54.6 MB
  • 07 Ensemble Machine Learning/070 Ensemble of ensembles Part 2.mp4 47.1 MB
  • 08 k-Nearest Neighbours kNN/071 kNN Introduction.mp4 4.6 MB
  • 08 k-Nearest Neighbours kNN/072 kNN Concepts.mp4 15.7 MB
  • 08 k-Nearest Neighbours kNN/073 kNN and Iris Dataset Demo.mp4 21.8 MB
  • 08 k-Nearest Neighbours kNN/074 Distance Metric.mp4 13.7 MB
  • 08 k-Nearest Neighbours kNN/075 Project Cancer Detection Part 1.mp4 51.8 MB
  • 08 k-Nearest Neighbours kNN/076 Project Cancer Detection Part 2.mp4 51.0 MB
  • 09 Dimensionality Reduction/077 Introduction.mp4 3.8 MB
  • 09 Dimensionality Reduction/078 Dimensionality Reduction Concept.mp4 27.0 MB
  • 09 Dimensionality Reduction/079 PCA Introduction.mp4 44.3 MB
  • 09 Dimensionality Reduction/080 Dimensionality Reduction Demo.mp4 15.3 MB
  • 09 Dimensionality Reduction/081 Project Wine 1 Dimensionality Reduction with PCA.mp4 48.4 MB
  • 09 Dimensionality Reduction/082 Project Abalone.html 458 Bytes
  • 09 Dimensionality Reduction/083 Project Wine 2 Choosing the Number of Components.mp4 19.7 MB
  • 09 Dimensionality Reduction/084 Kernel PCA.mp4 37.6 MB
  • 09 Dimensionality Reduction/085 Kernel PCA Demo.mp4 16.8 MB
  • 09 Dimensionality Reduction/086 LDA Comparison between LDA and PCA.mp4 17.7 MB
  • 10 Unsupervised Learning Clustering/087 Introduction.mp4 4.2 MB
  • 10 Unsupervised Learning Clustering/088 Clustering Concepts.mp4 18.3 MB
  • 10 Unsupervised Learning Clustering/089 MLextend.mp4 23.7 MB
  • 10 Unsupervised Learning Clustering/090 Wards Agglomerative Hierarchical Clustering.mp4 46.3 MB
  • 10 Unsupervised Learning Clustering/091 Truncating Dendrogram.mp4 59.2 MB
  • 10 Unsupervised Learning Clustering/092 k-Means Clustering.mp4 38.4 MB
  • 10 Unsupervised Learning Clustering/093 Elbow Method.mp4 16.5 MB
  • 10 Unsupervised Learning Clustering/094 Silhouette Analysis.mp4 17.3 MB
  • 10 Unsupervised Learning Clustering/095 Mean Shift.mp4 26.9 MB
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