[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science torrent, magnet, bt

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[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science

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1852

Content Size:

6.84GB

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2020-07-24

Keywords:

UdemyCourseDownloader Machine Learning Z™Hands On Python In Data Science

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File Name

Size

12 Logistic Regression/096 Logistic Regression in R - Step 5.mp4

93.76MB

udemycoursedownloader.com.url

132B

01 Welcome to the course/001 Applications of Machine Learning-en.srt

5.3KB

01 Welcome to the course/001 Applications of Machine Learning-es.srt

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01 Welcome to the course/001 Applications of Machine Learning-it.srt

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01 Welcome to the course/001 Applications of Machine Learning-ja.srt

6.58KB

01 Welcome to the course/001 Applications of Machine Learning-pt.srt

5.73KB

01 Welcome to the course/001 Applications of Machine Learning-tr.srt

5.48KB

01 Welcome to the course/001 Applications of Machine Learning.mp4

9.81MB

01 Welcome to the course/002 Why Machine Learning is the Future-en.srt

9.23KB

01 Welcome to the course/002 Why Machine Learning is the Future-es.srt

9.7KB

01 Welcome to the course/002 Why Machine Learning is the Future-it.srt

9.64KB

01 Welcome to the course/002 Why Machine Learning is the Future-ja.srt

10.69KB

01 Welcome to the course/002 Why Machine Learning is the Future-pt.srt

9.73KB

01 Welcome to the course/002 Why Machine Learning is the Future-tr.srt

9.24KB

01 Welcome to the course/002 Why Machine Learning is the Future.mp4

14.48MB

01 Welcome to the course/003 Important notes tips tricks for this course.html

4.16KB

01 Welcome to the course/004 Machine-Learning-A-Z-Q-A.pdf

2.26MB

01 Welcome to the course/004 This PDF resource will help you a lot.html

2.35KB

01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-en.srt

11.82KB

01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-es.srt

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01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-it.srt

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01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-ja.srt

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01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-pt.srt

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01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-tr.srt

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01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp4

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01 Welcome to the course/006 Update Recommended Anaconda Version.html

2.17KB

01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-en.srt

8.83KB

01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-es.srt

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01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-ja.srt

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01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-pt.srt

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01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-tr.srt

9.02KB

01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp4

23.21MB

01 Welcome to the course/008 BONUS Meet your instructors.html

1.89KB

02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-en.srt

2.43KB

02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-es.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-tr.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-it.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/013 For Python learners summary of Object-oriented programming classes objects.html

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02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/016 WARNING - Update.html

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02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-en.srt

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28.29KB

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02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp4

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02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-en.srt

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02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp4

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03 -------------------- Part 2 Regression --------------------/020 Welcome to Part 2 - Regression.html

1.69KB

04 Simple Linear Regression/021 How to get the dataset-en.srt

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04 Simple Linear Regression/022 Dataset Business Problem Description-en.srt

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04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-en.srt

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05 Multiple Linear Regression/034 Dataset Business Problem Description-en.srt

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19 Evaluating Classification Models Performance/128 False Positives False Negatives-en.srt

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19 Evaluating Classification Models Performance/133 Conclusion of Part 3 - Classification.html

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20 -------------------- Part 4 Clustering --------------------/134 Welcome to Part 4 - Clustering.html

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21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-en.srt

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22 Hierarchical Clustering/155 Conclusion of Part 4 - Clustering.html

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23 -------------------- Part 5 Association Rule Learning --------------------/156 Welcome to Part 5 - Association Rule Learning.html

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26 -------------------- Part 6 Reinforcement Learning --------------------/168 Welcome to Part 6 - Reinforcement Learning.html

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33 -------------------- Part 9 Dimensionality Reduction --------------------/259 Welcome to Part 9 - Dimensionality Reduction.html

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35 Linear Discriminant Analysis (LDA)/271 LDA in R-ja.srt

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35 Linear Discriminant Analysis (LDA)/271 LDA in R-pt.srt

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35 Linear Discriminant Analysis (LDA)/271 LDA in R-tr.srt

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35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp4

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36 Kernel PCA/272 How to get the dataset-en.srt

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36 Kernel PCA/272 How to get the dataset-es.srt

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36 Kernel PCA/272 How to get the dataset-it.srt

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36 Kernel PCA/272 How to get the dataset-ja.srt

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36 Kernel PCA/272 How to get the dataset-pt.srt

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36 Kernel PCA/272 How to get the dataset-tr.srt

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36 Kernel PCA/272 How to get the dataset.mp4

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36 Kernel PCA/273 Kernel PCA in Python-en.srt

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36 Kernel PCA/273 Kernel PCA in Python-es.srt

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36 Kernel PCA/273 Kernel PCA in Python-it.srt

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36 Kernel PCA/273 Kernel PCA in Python-pt.srt

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36 Kernel PCA/273 Kernel PCA in Python.mp4

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36 Kernel PCA/274 Kernel PCA in R-en.srt

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36 Kernel PCA/274 Kernel PCA in R-es.srt

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36 Kernel PCA/274 Kernel PCA in R-it.srt

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36 Kernel PCA/274 Kernel PCA in R-ja.srt

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36 Kernel PCA/274 Kernel PCA in R-pt.srt

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36 Kernel PCA/274 Kernel PCA in R.mp4

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37 -------------------- Part 10 Model Selection Boosting --------------------/275 Welcome to Part 10 - Model Selection Boosting.html

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38 Model Selection/276 How to get the dataset-en.srt

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38 Model Selection/276 How to get the dataset-es.srt

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38 Model Selection/276 How to get the dataset-it.srt

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38 Model Selection/276 How to get the dataset-ja.srt

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38 Model Selection/276 How to get the dataset-pt.srt

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38 Model Selection/276 How to get the dataset-tr.srt

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38 Model Selection/276 How to get the dataset.mp4

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38 Model Selection/277 k-Fold Cross Validation in Python-en.srt

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38 Model Selection/277 k-Fold Cross Validation in Python-es.srt

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38 Model Selection/277 k-Fold Cross Validation in Python-it.srt

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38 Model Selection/277 k-Fold Cross Validation in Python-pt.srt

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38 Model Selection/277 k-Fold Cross Validation in Python.mp4

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38 Model Selection/278 k-Fold Cross Validation in R-en.srt

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38 Model Selection/278 k-Fold Cross Validation in R-es.srt

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38 Model Selection/278 k-Fold Cross Validation in R-it.srt

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38 Model Selection/278 k-Fold Cross Validation in R.mp4

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38 Model Selection/279 Grid Search in Python - Step 1-en.srt

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38 Model Selection/280 Grid Search in Python - Step 2-en.srt

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38 Model Selection/280 Grid Search in Python - Step 2.mp4

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38 Model Selection/281 Grid Search in R-en.srt

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39 XGBoost/282 How to get the dataset-en.srt

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39 XGBoost/282 How to get the dataset-es.srt

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39 XGBoost/282 How to get the dataset-it.srt

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39 XGBoost/282 How to get the dataset-pt.srt

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39 XGBoost/282 How to get the dataset-tr.srt

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39 XGBoost/282 How to get the dataset.mp4

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39 XGBoost/283 XGBoost in Python - Step 1-en.srt

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39 XGBoost/283 XGBoost in Python - Step 1-es.srt

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39 XGBoost/283 XGBoost in Python - Step 1-it.srt

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39 XGBoost/284 XGBoost in Python - Step 2-en.srt

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39 XGBoost/285 XGBoost in R-en.srt

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39 XGBoost/285 XGBoost in R-it.srt

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39 XGBoost/285 XGBoost in R-pt.srt

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39 XGBoost/285 XGBoost in R.mp4

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40 Bonus Lectures/286 YOUR SPECIAL BONUS.html

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