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种子名称： [FreeTutorials.Us] Udemy - machine-learning-course-with-python
文件类型： 视频
文件数目： 89个文件
文件大小： 2.91 GB
收录时间： 2018-12-12 09:05
已经下载： 3次
资源热度： 99
最近下载： 2024-12-16 13:59

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

01 Introduction/001 What Does the Course Cover.mp49.88MB

02 Getting Started with Anaconda/004 Windows OS Downloading Installing Anaconda.mp464.36MB

02 Getting Started with Anaconda/005 Windows OS Managing Environment.mp418.82MB

02 Getting Started with Anaconda/008 Navigating the Spyder Jupyter Notebook Interface.mp441.35MB

02 Getting Started with Anaconda/009 Downloading the IRIS Datasets.mp410.49MB

02 Getting Started with Anaconda/010 Data Exploration and Analysis.mp434.79MB

02 Getting Started with Anaconda/011 Presenting Your Data.mp446.52MB

03 Regression/012 Introduction.mp411.72MB

03 Regression/013 Categories of Machine Learning.mp422.36MB

03 Regression/014 Working with Scikit-Learn.mp445.94MB

03 Regression/015 Boston Housing Data - EDA.mp461.46MB

03 Regression/016 Correlation Analysis and Feature Selection.mp422.58MB

03 Regression/017 Simple Linear Regression Modelling with Boston Housing Data.mp433.8MB

03 Regression/018 Robust Regression.mp433.46MB

03 Regression/019 Evaluate Model Performance.mp459.14MB

03 Regression/020 Multiple Regression with statsmodel.mp459.64MB

03 Regression/021 Multiple Regression and Feature Importance.mp444.66MB

03 Regression/022 Ordinary Least Square Regression and Gradient Descent.mp450.03MB

03 Regression/023 Regularised Method for Regression.mp447.81MB

03 Regression/024 Polynomial Regression.mp443.81MB

03 Regression/025 Dealing with Non-linear relationships.mp428.44MB

03 Regression/026 Feature Importance Revisited.mp426.18MB

03 Regression/027 Data Pre-Processing 1.mp434.92MB

03 Regression/028 Data Pre-Processing 2.mp449.58MB

03 Regression/029 Variance Bias Trade Off - Validation Curve.mp443.83MB

03 Regression/030 Variance Bias Trade Off - Learning Curve.mp442.45MB

03 Regression/031 Cross Validation.mp444.68MB

04 Classification/032 Introduction.mp47.91MB

04 Classification/033 Logistic Regression 1.mp426.93MB

04 Classification/034 Logistic Regression 2.mp441.75MB

04 Classification/035 MNIST Project 1 - Introduction.mp434.55MB

04 Classification/036 MNIST Project 2 - SGDClassifier.mp425.47MB

04 Classification/037 MNIST Project 3 - Performance Measures.mp426.4MB

04 Classification/038 MNIST Project 4 - Confusion Matrix Precision Recall and F1 Score.mp446.08MB

04 Classification/039 MNIST Project 5 - Precision and Recall Tradeoff.mp443.91MB

04 Classification/040 MNIST Project 6 - The ROC Curve.mp433.78MB

05 Support Vector Machine SVM/042 Introduction.mp44.72MB

05 Support Vector Machine SVM/043 Support Vector Machine SVM Concepts.mp451.8MB

05 Support Vector Machine SVM/044 Linear SVM Classification.mp431.57MB

05 Support Vector Machine SVM/045 Polynomial Kernel.mp450.31MB

05 Support Vector Machine SVM/046 Gaussian Radial Basis Function.mp444.8MB

05 Support Vector Machine SVM/047 Support Vector Regression.mp417.39MB

05 Support Vector Machine SVM/048 Advantages and Disadvantages of SVM.mp413.11MB

06 Tree/049 Introduction.mp45.58MB

06 Tree/050 What is Decision Tree.mp434.07MB

06 Tree/051 Training a Decision Tree.mp416.59MB

06 Tree/052 Visualising a Decision Trees.mp455.25MB

06 Tree/053 Decision Tree Learning Algorithm.mp436.97MB

06 Tree/054 Decision Tree Regression.mp433.8MB

06 Tree/055 Overfitting and Grid Search.mp454.43MB

06 Tree/056 Where to From Here.mp411.62MB

06 Tree/057 Project HR - Loading and preprocesing data.mp456.76MB

06 Tree/058 Project HR - Modelling.mp416.5MB

07 Ensemble Machine Learning/059 Introduction.mp45.22MB

07 Ensemble Machine Learning/060 Ensemble Learning Methods Introduction.mp427.68MB

07 Ensemble Machine Learning/061 Bagging Part 1.mp455.47MB

07 Ensemble Machine Learning/062 Bagging Part 2.mp437.31MB

07 Ensemble Machine Learning/063 Random Forests.mp443.11MB

07 Ensemble Machine Learning/064 Extra-Trees.mp421.58MB

07 Ensemble Machine Learning/065 AdaBoost.mp439.82MB

07 Ensemble Machine Learning/066 Gradient Boosting Machine.mp444.87MB

07 Ensemble Machine Learning/067 XGBoost.mp451.35MB

07 Ensemble Machine Learning/068 Project HR - Human Resources Analytics.mp488.16MB

07 Ensemble Machine Learning/069 Ensemble of ensembles Part 1.mp452.03MB

07 Ensemble Machine Learning/070 Ensemble of ensembles Part 2.mp444.89MB

08 k-Nearest Neighbours kNN/071 kNN Introduction.mp44.37MB

08 k-Nearest Neighbours kNN/072 kNN Concepts.mp415MB

08 k-Nearest Neighbours kNN/073 kNN and Iris Dataset Demo.mp420.75MB

08 k-Nearest Neighbours kNN/074 Distance Metric.mp413.08MB

08 k-Nearest Neighbours kNN/075 Project Cancer Detection Part 1.mp449.4MB

08 k-Nearest Neighbours kNN/076 Project Cancer Detection Part 2.mp448.64MB

09 Dimensionality Reduction/077 Introduction.mp43.63MB

09 Dimensionality Reduction/078 Dimensionality Reduction Concept.mp425.73MB

09 Dimensionality Reduction/079 PCA Introduction.mp442.24MB

09 Dimensionality Reduction/080 Dimensionality Reduction Demo.mp414.64MB

09 Dimensionality Reduction/081 Project Wine 1 Dimensionality Reduction with PCA.mp446.11MB

09 Dimensionality Reduction/083 Project Wine 2 Choosing the Number of Components.mp418.79MB

09 Dimensionality Reduction/084 Kernel PCA.mp435.84MB

09 Dimensionality Reduction/085 Kernel PCA Demo.mp416.01MB

09 Dimensionality Reduction/086 LDA Comparison between LDA and PCA.mp416.9MB

10 Unsupervised Learning Clustering/087 Introduction.mp44.03MB

10 Unsupervised Learning Clustering/088 Clustering Concepts.mp417.47MB

10 Unsupervised Learning Clustering/089 MLextend.mp422.58MB

10 Unsupervised Learning Clustering/090 Wards Agglomerative Hierarchical Clustering.mp444.12MB

10 Unsupervised Learning Clustering/091 Truncating Dendrogram.mp456.42MB

10 Unsupervised Learning Clustering/092 k-Means Clustering.mp436.59MB

10 Unsupervised Learning Clustering/093 Elbow Method.mp415.76MB

10 Unsupervised Learning Clustering/094 Silhouette Analysis.mp416.5MB

10 Unsupervised Learning Clustering/095 Mean Shift.mp425.68MB