GetFreeCourses.Co-Udemy-The Complete Machine Learning Course with Python

BT种子名称

分享给好友

BT种子基本信息

种子哈希：139dbc3cd99b898f266f50100632559aa986fe91
文档大小：7.3 GB
文档个数：230个文档
下载次数：1162次
下载速度：极快
收录时间：2021-03-20
最近下载：2025-05-15
DMCA/屏蔽：DMCA/屏蔽

下载磁力链接

magnet:?xt=urn:btih:139DBC3CD99B898F266F50100632559AA986FE91
复制磁力链接到utorrent、Bitcomet、迅雷、115、百度网盘等下载工具进行下载。

下载BT种子

磁力链接种子下载迅雷下载二维码迅雷看看
磁力链接在线播放？试试百度云网盘离线下载播放！

GetFreeCourses.Co-Udemy-The Complete Machine Learning Course with Python的二维码

文档列表

6. Tree/6. Project HR.mp4 186.5 MB
7. Ensemble Machine Learning/2. Bagging.mp4 173.5 MB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 163.2 MB
3. Regression/2. EDA.mp4 159.0 MB
11. Deep Learning/3. Motivational Example - Project MNIST.mp4 152.0 MB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 150.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 148.8 MB
3. Regression/15. Data Preprocessing.mp4 142.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 134.8 MB
3. Regression/19. CV Illustration.mp4 133.4 MB
10. Unsupervised Learning Clustering/1. Clustering.mp4 131.8 MB
3. Regression/9. Multiple Regression 1.mp4 131.6 MB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 130.9 MB
4. Classification/1. Logistic Regression.mp4 125.4 MB
3. Regression/7. Robust Regression.mp4 124.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 116.5 MB
3. Regression/12. Polynomial Regression.mp4 116.2 MB
4. Classification/3. Understanding MNIST.mp4 114.3 MB
3. Regression/4. Correlation Analysis and Feature Selection.mp4 110.3 MB
4. Classification/10. Precision Recall Tradeoff.mp4 107.0 MB
3. Regression/8. Evaluate Regression Model Performance.mp4 104.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 101.7 MB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 98.0 MB
3. Regression/10. Multiple Regression 2.mp4 95.6 MB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 94.2 MB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 93.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 92.4 MB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 88.5 MB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 84.9 MB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 84.2 MB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 83.6 MB
3. Regression/6. Five Steps Machine Learning Process.mp4 81.0 MB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 81.0 MB
3. Regression/5. Linear Regression with Scikit-Learn.mp4 80.7 MB
11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 79.7 MB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 79.4 MB
11. Deep Learning/4. Binary Classification Problem.mp4 75.6 MB
5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 73.5 MB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 73.5 MB
3. Regression/16. Variance-Bias Trade Off.mp4 72.0 MB
6. Tree/7. Project HR with Google Colab.mp4 69.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 69.4 MB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 67.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 66.7 MB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 66.0 MB
3. Regression/13. Dealing with Non-linear Relationships.mp4 65.7 MB
5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 62.6 MB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 62.1 MB
10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 60.5 MB
4. Classification/4. SGD.mp4 60.1 MB
3. Regression/17. Learning Curve.mp4 59.1 MB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 58.6 MB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 57.6 MB
6. Tree/3. Visualizing Boundary.mp4 57.4 MB
4. Classification/6. Confusion Matrix.mp4 57.4 MB
1. Introduction/1. What Does the Course Cover.mp4 57.0 MB
4. Classification/12. ROC.mp4 54.8 MB
4. Classification/5. Performance Measure and Stratified k-Fold.mp4 54.0 MB
6. Tree/2. Training and Visualizing a Decision Tree.mp4 53.9 MB
2. Getting Started with Anaconda/2. Hello World.mp4 53.7 MB
7. Ensemble Machine Learning/4. AdaBoost.mp4 52.3 MB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 51.8 MB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 51.4 MB
3. Regression/1. Scikit-Learn.mp4 50.8 MB
3. Regression/18. Cross Validation.mp4 50.4 MB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 50.2 MB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 48.7 MB
3. Regression/11. Regularized Regression.mp4 46.5 MB
6. Tree/1. Introduction to Decision Tree.mp4 46.0 MB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 45.9 MB
4. Classification/2. Introduction to Classification.mp4 44.2 MB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 42.6 MB
6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 42.0 MB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 40.3 MB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 39.7 MB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 39.7 MB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 39.3 MB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 39.0 MB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 38.4 MB
3. Regression/14. Feature Importance.mp4 38.0 MB
6. Tree/5. End to End Modeling.mp4 37.3 MB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 37.1 MB
7. Ensemble Machine Learning/7. XGBoost.mp4 36.8 MB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 36.7 MB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 35.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 33.9 MB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 32.9 MB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 32.2 MB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 31.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 30.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 29.9 MB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 28.8 MB
4. Classification/7. Precision.mp4 24.7 MB
3. Regression/3. Correlation Analysis and Feature Selection.mp4 23.7 MB
11. Deep Learning/2. Neural Network Architecture.mp4 23.5 MB
7. Ensemble Machine Learning/6. XGBoost Installation.mp4 23.3 MB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 23.0 MB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 22.5 MB
4. Classification/11. Altering the Precision Recall Tradeoff.mp4 21.9 MB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 21.9 MB
4. Classification/8. Recall.mp4 20.6 MB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 19.6 MB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 17.7 MB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14.8 MB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 14.4 MB
4. Classification/9. f1.mp4 12.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11.7 MB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9.5 MB
3. Regression/3.1 0305.zip 2.2 MB
8. k-Nearest Neighbours (kNN)/4.1 0805.zip 41.7 kB
6. Tree/6. Project HR.srt 31.5 kB
3. Regression/15. Data Preprocessing.srt 28.8 kB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.srt 27.0 kB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.srt 26.9 kB
11. Deep Learning/3. Motivational Example - Project MNIST.srt 26.4 kB
4. Classification/1. Logistic Regression.srt 26.0 kB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.srt 25.1 kB
3. Regression/2. EDA.srt 25.0 kB
3. Regression/9. Multiple Regression 1.srt 24.9 kB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.srt 24.4 kB
7. Ensemble Machine Learning/2. Bagging.srt 23.4 kB
4. Classification/10. Precision Recall Tradeoff.srt 22.8 kB
3. Regression/12. Polynomial Regression.srt 22.6 kB
3. Regression/7. Robust Regression.srt 22.3 kB
3. Regression/19. CV Illustration.srt 21.8 kB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.srt 21.5 kB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.srt 21.4 kB
10. Unsupervised Learning Clustering/1. Clustering.srt 21.2 kB
3. Regression/8. Evaluate Regression Model Performance.srt 19.6 kB
4. Classification/3. Understanding MNIST.srt 18.7 kB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.srt 18.6 kB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.srt 17.9 kB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.srt 17.1 kB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.srt 16.4 kB
3. Regression/5. Linear Regression with Scikit-Learn.srt 16.4 kB
3. Regression/10. Multiple Regression 2.srt 15.8 kB
3. Regression/4. Correlation Analysis and Feature Selection.srt 15.6 kB
3. Regression/16. Variance-Bias Trade Off.srt 14.9 kB
2. Getting Started with Anaconda/2. Hello World.srt 14.3 kB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.srt 14.2 kB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.srt 14.1 kB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.srt 14.1 kB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.srt 13.4 kB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.srt 13.3 kB
11. Deep Learning/5. Natural Language Processing - Binary Classification.srt 13.1 kB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.srt 13.0 kB
6. Tree/7. Project HR with Google Colab.srt 13.0 kB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.srt 12.9 kB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.srt 12.7 kB
11. Deep Learning/4. Binary Classification Problem.srt 12.5 kB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.srt 12.4 kB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.srt 12.3 kB
4. Classification/6. Confusion Matrix.srt 12.0 kB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.srt 11.8 kB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.srt 11.8 kB
4. Classification/4. SGD.srt 11.8 kB
3. Regression/13. Dealing with Non-linear Relationships.srt 11.3 kB
3. Regression/1. Scikit-Learn.srt 11.2 kB
3. Regression/17. Learning Curve.srt 11.1 kB
10. Unsupervised Learning Clustering/2. k_Means Clustering.srt 11.1 kB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.srt 11.1 kB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.srt 11.0 kB
3. Regression/3. Correlation Analysis and Feature Selection.srt 10.9 kB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.srt 10.8 kB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.srt 10.7 kB
3. Regression/18. Cross Validation.srt 10.5 kB
3. Regression/6. Five Steps Machine Learning Process.srt 10.2 kB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.srt 10.2 kB
5. Support Vector Machine (SVM)/5. Support Vector Regression.srt 10.0 kB
6. Tree/3. Visualizing Boundary.srt 9.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.srt 9.7 kB
5. Support Vector Machine (SVM)/4. Radial Basis Function.srt 9.6 kB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.srt 9.0 kB
4. Classification/5. Performance Measure and Stratified k-Fold.srt 8.9 kB
6. Tree/1. Introduction to Decision Tree.srt 8.9 kB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.srt 8.8 kB
3. Regression/11. Regularized Regression.srt 8.7 kB
4. Classification/12. ROC.srt 8.4 kB
7. Ensemble Machine Learning/4. AdaBoost.srt 8.4 kB
11. Deep Learning/2. Neural Network Architecture.srt 8.1 kB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.srt 8.0 kB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.srt 8.0 kB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.srt 7.7 kB
6. Tree/2. Training and Visualizing a Decision Tree.srt 7.6 kB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.srt 7.5 kB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.srt 7.0 kB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.srt 6.9 kB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.srt 6.9 kB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.srt 6.7 kB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.srt 6.6 kB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.srt 6.3 kB
4. Classification/2. Introduction to Classification.srt 6.2 kB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.srt 6.1 kB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.srt 6.0 kB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.srt 5.9 kB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.srt 5.8 kB
3. Regression/14. Feature Importance.srt 5.8 kB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.srt 5.8 kB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.srt 5.8 kB
6. Tree/4. Tree Regression, Regularization and Over Fitting.srt 5.7 kB
6. Tree/5. End to End Modeling.srt 5.7 kB
7. Ensemble Machine Learning/7. XGBoost.srt 5.5 kB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.srt 5.2 kB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.srt 4.9 kB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.srt 4.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.srt 4.7 kB
4. Classification/7. Precision.srt 4.5 kB
4. Classification/8. Recall.srt 4.0 kB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.srt 4.0 kB
4. Classification/11. Altering the Precision Recall Tradeoff.srt 3.8 kB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.srt 3.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.srt 3.7 kB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.srt 3.5 kB
1. Introduction/1. What Does the Course Cover.srt 3.2 kB
7. Ensemble Machine Learning/6. XGBoost Installation.srt 3.1 kB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.srt 2.8 kB
4. Classification/9. f1.srt 2.4 kB
1. Introduction/2. How to Succeed in This Course.html 2.3 kB
1. Introduction/3. Project Files and Resources.html 2.1 kB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.srt 1.9 kB
8. k-Nearest Neighbours (kNN)/3. Addition Materials.html 335 Bytes
13. Computer Vision and Convolutional Neural Network (CNN)/How you can help GetFreeCourses.Co.txt 182 Bytes
7. Ensemble Machine Learning/How you can help GetFreeCourses.Co.txt 182 Bytes
How you can help GetFreeCourses.Co.txt 182 Bytes
13. Computer Vision and Convolutional Neural Network (CNN)/Download Paid Udemy Courses For Free.url 116 Bytes
13. Computer Vision and Convolutional Neural Network (CNN)/GetFreeCourses.Co.url 116 Bytes
7. Ensemble Machine Learning/Download Paid Udemy Courses For Free.url 116 Bytes
7. Ensemble Machine Learning/GetFreeCourses.Co.url 116 Bytes
Download Paid Udemy Courses For Free.url 116 Bytes
GetFreeCourses.Co.url 116 Bytes

==查看完整文档列表==

24小时热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-EniaHD.mkv 5077次下载 WEB DL DDP5
The.Super.Cube.S01E09.1080p.iQ.WEB-DL.AAC2.0.H.264-VARYG.mkv 5040次下载 WEB DL Cube
Star.Wars.Andor.S02E05.I.Have.Friends.Everywhere.1080p.DS... 4944次下载 WEB DL Star
新桥恋人.电影港地址发布页 www.dygang.me 收藏不迷路 4365次下载 me www 发布页
Night at the Museum Battle of the Smithsonian 2009 2160p... 4161次下载 KiNGDOM WEB DL
www.UIndex.org - ... 4126次下载 www MeGusta HEVC
Palma.2.2024.WEB-DL.1080p.ExKinoRay.mkv 4000次下载 WEB DL 2.2024
Guns N' Roses - Greatest Hits Live Broadcast Collection... 3915次下载 Live Remastered Hits

本周热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
(同人CG集) [白色絵の具 (無味ムスミ)] 友達の母が巨乳で金髪の淫乱美女でした.zip 6346次下载 zip 巨乳 CG
You.S05E02.720p.HEVC.x265-MeGusta[EZTVx.to].mkv 6221次下载 S05E02 HEVC 720p
up-mod-minecraft-play-with-friends-v1-21-80-28-... 6023次下载 982108028 mod play
the.last.of.us.s02e02.1080p.web.h264-successful... 5335次下载 web last h264
异世降临.6v电影地址发布页 www.6v123.net 收藏不迷路 5220次下载 www 发布页 6v123
Opasnaya.blizost.S01.2025.WEB-DL.1080p 5127次下载 WEB DL S01
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Andor.S02E02.Sagrona.Teema.1080p.HEVC.x265-MeGu... 5100次下载 HEVC Andor mkv
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-E... 5077次下载 WEB DL DDP5

种子标签

Co Complete Python Udemy Machine Course GetFreeCourses Learning

种子评价

优质的种子 (0) 假种子 (0) 有密码 (0) 低质量 (0) 有病毒 (0) 无法下载 (0)
欢迎对种子质量进行评价。

人气女优

北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

分享BT种子/磁力链接

亲，你知道吗？下载的人越多速度越快，赶快把本页面分享给好友一起下载吧^_^

磁力管家