[DesireCourse.Net] Udemy - The Complete Machine Learning Course with Python

Torrent Hash:
4105824974F98A3C8A55EF524A209615BA9B11BF
Number of Files:
223
Content Size:
6.79GB
Convert On:
2020-09-10
Keywords:
DesireCourseNetUdemyTheCompleteMachineLearningCoursewithPython
Magnet Link:
Download
Play Now
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File Name
Size
1. Introduction/1. What Does the Course Cover.mp4
54.4MB
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1. Introduction/1. What Does the Course Cover.vtt
2.97KB
1. Introduction/2. How to Succeed in This Course.html
2.22KB
1. Introduction/3. Project Files and Resources.html
1.71KB
10. Unsupervised Learning Clustering/1. Clustering.mp4
125.68MB
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10. Unsupervised Learning Clustering/1. Clustering.vtt
18.73KB
10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4
57.72MB
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10. Unsupervised Learning Clustering/2. k_Means Clustering.vtt
9.98KB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4
143.85MB
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11. Deep Learning/1. Estimating Simple Function with Neural Networks.vtt
24.36KB
11. Deep Learning/2. Neural Network Architecture.mp4
22.38MB
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11. Deep Learning/2. Neural Network Architecture.vtt
7.22KB
11. Deep Learning/3. Motivational Example - Project MNIST.mp4
144.96MB
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11. Deep Learning/3. Motivational Example - Project MNIST.vtt
23.52KB
11. Deep Learning/4. Binary Classification Problem.mp4
72.11MB
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11. Deep Learning/4. Binary Classification Problem.vtt
11.47KB
11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4
76.05MB
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11. Deep Learning/5. Natural Language Processing - Binary Classification.vtt
11.74KB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4
13.75MB
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12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.vtt
2.54KB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4
54.96MB
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12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.vtt
12.65KB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4
18.67MB
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12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.vtt
5.13KB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4
37.47MB
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12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.vtt
11.22KB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4
70.06MB
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12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.vtt
16.68KB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4
27.44MB
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12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.vtt
5.31KB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4
20.85MB
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12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.vtt
4.87KB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4
77.24MB
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12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.vtt
11.5KB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4
155.61MB
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12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.vtt
23.08KB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4
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12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4
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12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4
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12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4
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13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4
141.94MB
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15.37KB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4
30.03MB
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6.73KB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4
29.13MB
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6.22KB
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84.39MB
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18.46KB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4
32.32MB
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6.86KB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4
88.13MB
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12.51KB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4
38.42MB
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5.99KB
2. Getting Started with Anaconda/2. Hello World.mp4
51.22MB
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12.5KB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4
89.84MB
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14.45KB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4
64.56MB
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10.04KB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4
55.87MB
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9.92KB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4
93.49MB
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11.52KB
3. Regression/1. Scikit-Learn.mp4
48.45MB
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3. Regression/1. Scikit-Learn.vtt
9.97KB
3. Regression/10. Multiple Regression 2.mp4
91.15MB
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3. Regression/10. Multiple Regression 2.vtt
13.81KB
3. Regression/11. Regularized Regression.mp4
44.35MB
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3. Regression/11. Regularized Regression.vtt
7.79KB
3. Regression/12. Polynomial Regression.mp4
110.78MB
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3. Regression/12. Polynomial Regression.vtt
19.69KB
3. Regression/13. Dealing with Non-linear Relationships.mp4
62.69MB
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3. Regression/13. Dealing with Non-linear Relationships.vtt
10.27KB
3. Regression/14. Feature Importance.mp4
36.25MB
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3. Regression/14. Feature Importance.vtt
5.41KB
3. Regression/15. Data Preprocessing.mp4
135.55MB
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3. Regression/16. Variance-Bias Trade Off.mp4
68.7MB
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13.71KB
3. Regression/17. Learning Curve.mp4
56.37MB
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10.24KB
3. Regression/18. Cross Validation.mp4
48.04MB
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9.71KB
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127.23MB
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19.86KB
3. Regression/2. EDA.mp4
151.67MB
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22.43KB
3. Regression/3. Correlation Analysis and Feature Selection.mp4
22.58MB
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9.81KB
3. Regression/3.1 0305.zip.zip
2.13MB
3. Regression/4. Correlation Analysis and Feature Selection.mp4
105.19MB
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13.94KB
3. Regression/5. Linear Regression with Scikit-Learn.mp4
76.98MB
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14.94KB
3. Regression/6. Five Steps Machine Learning Process.mp4
77.27MB
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9.17KB
3. Regression/7. Robust Regression.mp4
119.06MB
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20.1KB
3. Regression/8. Evaluate Regression Model Performance.mp4
99.66MB
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17.88KB
3. Regression/9. Multiple Regression 1.mp4
125.51MB
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22.46KB
4. Classification/1. Logistic Regression.mp4
119.59MB
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23.48KB
4. Classification/10. Precision Recall Tradeoff.mp4
102.01MB
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4. Classification/10. Precision Recall Tradeoff.vtt
20.77KB
4. Classification/11. Altering the Precision Recall Tradeoff.mp4
20.93MB
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3.5KB
4. Classification/12. ROC.mp4
52.22MB
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4. Classification/12. ROC.vtt
7.65KB
4. Classification/2. Introduction to Classification.mp4
42.12MB
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4. Classification/2. Introduction to Classification.vtt
5.74KB
4. Classification/3. Understanding MNIST.mp4
108.98MB
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16.38KB
4. Classification/4. SGD.mp4
57.3MB
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10.57KB
4. Classification/5. Performance Measure and Stratified k-Fold.mp4
51.54MB
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8.12KB
4. Classification/6. Confusion Matrix.mp4
54.71MB
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11.05KB
4. Classification/7. Precision.mp4
23.59MB
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4.08KB
4. Classification/8. Recall.mp4
19.64MB
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3.65KB
4. Classification/9. f1.mp4
12.11MB
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2.28KB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4
37.87MB
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8.02KB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4
80.94MB
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12.11KB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4
34.96MB
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5.46KB
5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4
70.13MB
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8.75KB
5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4
59.68MB
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9.25KB
6. Tree/1. Introduction to Decision Tree.mp4
43.86MB
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7.9KB
6. Tree/2. Training and Visualizing a Decision Tree.mp4
51.4MB
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6.98KB
6. Tree/3. Visualizing Boundary.mp4
54.72MB
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8.82KB
6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4
40.05MB
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5.28KB
6. Tree/5. End to End Modeling.mp4
35.62MB
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5.34KB
6. Tree/6. Project HR.mp4
177.83MB
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6. Tree/6. Project HR.vtt
28.1KB
6. Tree/7. Project HR with Google Colab.mp4
66.57MB
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11.44KB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4
37.17MB
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5.56KB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4
37.85MB
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5.71KB
7. Ensemble Machine Learning/2. Bagging.mp4
165.44MB
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21.1KB
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7.89KB
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21.96MB
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3.58KB
7. Ensemble Machine Learning/6. XGBoost Installation.mp4
22.26MB
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2.81KB
7. Ensemble Machine Learning/7. XGBoost.mp4
35.05MB
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7. Ensemble Machine Learning/7. XGBoost.vtt
5.11KB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4
59.21MB
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9.5KB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4
46.4MB
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7.31KB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4
62.95MB
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11.02KB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4
75.73MB
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9.98KB
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335B
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49.4MB
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22.07KB
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40.76KB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4
31.37MB
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5.28KB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4
49.03MB
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8.19KB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4
47.87MB
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9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.vtt
6.98KB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4
36.61MB
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9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.vtt
6.05KB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4
21.44MB
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9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.vtt
3.65KB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4
34.15MB
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9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.vtt
5.85KB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4
30.74MB
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9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.vtt
4.33KB
[CourseClub.Me].url
48B
[DesireCourse.Net].url
51B

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