FreeAllCourse Com Udemy The Complete Machine Learning Course with Python

FreeAllCourse Com Udemy The Complete Machine Learning Course with Python

Name	[FreeAllCourse.Com] Udemy- The Complete Machine Learning Course with Python	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	7.3 GB
Total Files	221
Hash	BE1C9559DDC8EFB105665A8D97ABCA77B961D8C9

/1. Introduction/
1. What Does the Course Cover.mp4	57.0 MB
1. What Does the Course Cover.vtt	3.0 KB
2. How to Succeed in This Course.html	2.3 KB
3. Project Files and Resources.html	1.8 KB
/10. Unsupervised Learning Clustering/
1. Clustering.mp4	131.8 MB
1. Clustering.vtt	19.2 KB
2. k_Means Clustering.mp4	60.5 MB
2. k_Means Clustering.vtt	10.2 KB
/11. Deep Learning/
1. Estimating Simple Function with Neural Networks.mp4	150.8 MB
1. Estimating Simple Function with Neural Networks.vtt	24.9 KB
2. Neural Network Architecture.mp4	23.5 MB
2. Neural Network Architecture.vtt	7.4 KB
3. Motivational Example - Project MNIST.mp4	152.0 MB
3. Motivational Example - Project MNIST.vtt	24.1 KB
4. Binary Classification Problem.mp4	75.6 MB
4. Binary Classification Problem.vtt	11.7 KB
5. Natural Language Processing - Binary Classification.mp4	79.7 MB
5. Natural Language Processing - Binary Classification.vtt	12.0 KB
/12. Appendix A1 Foundations of Deep Learning/
1. Introduction to Neural Networks.mp4	14.4 MB
1. Introduction to Neural Networks.vtt	2.6 KB
10. Gradient Based Optimization.mp4	57.6 MB
10. Gradient Based Optimization.vtt	13.0 KB
11. Getting Started with Neural Network and Deep Learning Libraries.mp4	19.6 MB
11. Getting Started with Neural Network and Deep Learning Libraries.vtt	5.2 KB
12. Categories of Machine Learning.mp4	39.3 MB
12. Categories of Machine Learning.vtt	11.5 KB
13. Over and Under Fitting.mp4	73.5 MB
13. Over and Under Fitting.vtt	17.1 KB
14. Machine Learning Workflow.mp4	28.8 MB
14. Machine Learning Workflow.vtt	5.4 KB
2. Differences between Classical Programming and Machine Learning.mp4	21.9 MB
2. Differences between Classical Programming and Machine Learning.vtt	5.0 KB
3. Learning Representations.mp4	81.0 MB
3. Learning Representations.vtt	11.8 KB
4. What is Deep Learning.mp4	163.2 MB
4. What is Deep Learning.vtt	23.6 KB
5. Learning Neural Networks.mp4	42.6 MB
5. Learning Neural Networks.vtt	11.7 KB
6. Why Now.mp4	9.5 MB
6. Why Now.vtt	3.1 KB
7. Building Block Introduction.mp4	14.8 MB
7. Building Block Introduction.vtt	5.2 KB
8. Tensors.mp4	17.7 MB
8. Tensors.vtt	4.4 KB
9. Tensor Operations.mp4	93.1 MB
9. Tensor Operations.vtt	19.3 KB
/13. Computer Vision and Convolutional Neural Network (CNN)/
1. Outline.mp4	66.8 MB
1. Outline.vtt	4.2 KB
10. Training Your CNN 1.mp4	130.9 MB
10. Training Your CNN 1.vtt	15.6 KB
11. Training Your CNN 2.mp4	134.8 MB
11. Training Your CNN 2.vtt	22.9 KB
12. Loading Previously Trained Model.mp4	11.8 MB
12. Loading Previously Trained Model.vtt	1.6 KB
13. Model Performance Comparison.mp4	83.6 MB
13. Model Performance Comparison.vtt	10.9 KB
14. Data Augmentation.mp4	29.9 MB
14. Data Augmentation.vtt	3.4 KB
15. Transfer Learning.mp4	101.7 MB
15. Transfer Learning.vtt	12.4 KB
16. Feature Extraction.mp4	116.5 MB
16. Feature Extraction.vtt	13.2 KB
17. State of the Art Tools.mp4	37.1 MB
17. State of the Art Tools.vtt	6.1 KB
2. Neural Network Revision.mp4	45.9 MB
2. Neural Network Revision.vtt	9.4 KB
3. Motivational Example.mp4	69.4 MB
3. Motivational Example.vtt	8.9 KB
4. Visualizing CNN.mp4	148.8 MB
4. Visualizing CNN.vtt	15.7 KB
5. Understanding CNN.mp4	31.5 MB
5. Understanding CNN.vtt	6.9 KB
6. Layer - Input.mp4	30.5 MB
6. Layer - Input.vtt	6.4 KB
7. Layer - Filter.mp4	88.5 MB
7. Layer - Filter.vtt	18.9 KB
8. Activation Function.mp4	33.9 MB
8. Activation Function.vtt	7.0 KB
9. Pooling, Flatten, Dense.mp4	92.4 MB
9. Pooling, Flatten, Dense.vtt	12.8 KB
/2. Getting Started with Anaconda/
1. Installing Applications and Creating Environment.mp4	40.3 MB
1. Installing Applications and Creating Environment.vtt	6.1 KB
2. Hello World.mp4	53.7 MB
2. Hello World.vtt	12.8 KB
3. Iris Project 1 Working with Error Messages.mp4	94.2 MB
3. Iris Project 1 Working with Error Messages.vtt	14.8 KB
4. Iris Project 2 Reading CSV Data into Memory.mp4	67.7 MB
4. Iris Project 2 Reading CSV Data into Memory.vtt	10.3 KB
5. Iris Project 3 Loading data from Seaborn.mp4	58.6 MB
5. Iris Project 3 Loading data from Seaborn.vtt	10.2 KB
6. Iris Project 4 Visualization.mp4	98.0 MB
6. Iris Project 4 Visualization.vtt	11.8 KB
/3. Regression/
1. Scikit-Learn.mp4	50.8 MB
1. Scikit-Learn.vtt	10.2 KB
10. Multiple Regression 2.mp4	95.6 MB
10. Multiple Regression 2.vtt	14.1 KB
11. Regularized Regression.mp4	46.5 MB
11. Regularized Regression.vtt	8.0 KB
12. Polynomial Regression.mp4	116.2 MB
12. Polynomial Regression.vtt	20.2 KB
13. Dealing with Non-linear Relationships.mp4	65.7 MB
13. Dealing with Non-linear Relationships.vtt	10.5 KB
14. Feature Importance.mp4	38.0 MB
14. Feature Importance.vtt	5.5 KB
15. Data Preprocessing.mp4	142.1 MB
15. Data Preprocessing.vtt	26.1 KB
16. Variance-Bias Trade Off.mp4	72.0 MB
16. Variance-Bias Trade Off.vtt	14.0 KB
17. Learning Curve.mp4	59.1 MB
17. Learning Curve.vtt	10.5 KB
18. Cross Validation.mp4	50.4 MB
18. Cross Validation.vtt	9.9 KB
19. CV Illustration.mp4	133.4 MB
19. CV Illustration.vtt	20.3 KB
2. EDA.mp4	159.0 MB
2. EDA.vtt	23.0 KB
3. Correlation Analysis and Feature Selection.mp4	23.7 MB
3. Correlation Analysis and Feature Selection.vtt	10.0 KB
3.1 0305.zip.zip	2.2 MB
4. Correlation Analysis and Feature Selection.mp4	110.3 MB
4. Correlation Analysis and Feature Selection.vtt	14.3 KB
5. Linear Regression with Scikit-Learn.mp4	80.7 MB
5. Linear Regression with Scikit-Learn.vtt	15.3 KB
6. Five Steps Machine Learning Process.mp4	81.0 MB
6. Five Steps Machine Learning Process.vtt	9.4 KB
7. Robust Regression.mp4	124.8 MB
7. Robust Regression.vtt	20.6 KB
8. Evaluate Regression Model Performance.mp4	104.5 MB
8. Evaluate Regression Model Performance.vtt	18.3 KB
9. Multiple Regression 1.mp4	131.6 MB
9. Multiple Regression 1.vtt	23.0 KB
/4. Classification/
1. Logistic Regression.mp4	125.4 MB
1. Logistic Regression.vtt	24.0 KB
10. Precision Recall Tradeoff.mp4	107.0 MB
10. Precision Recall Tradeoff.vtt	21.3 KB
11. Altering the Precision Recall Tradeoff.mp4	21.9 MB
11. Altering the Precision Recall Tradeoff.vtt	3.6 KB
12. ROC.mp4	54.8 MB
12. ROC.vtt	7.8 KB
2. Introduction to Classification.mp4	44.2 MB
2. Introduction to Classification.vtt	5.9 KB
3. Understanding MNIST.mp4	114.3 MB
3. Understanding MNIST.vtt	16.8 KB
4. SGD.mp4	60.1 MB
4. SGD.vtt	10.8 KB
5. Performance Measure and Stratified k-Fold.mp4	54.0 MB
5. Performance Measure and Stratified k-Fold.vtt	8.3 KB
6. Confusion Matrix.mp4	57.4 MB
6. Confusion Matrix.vtt	11.3 KB
7. Precision.mp4	24.7 MB
7. Precision.vtt	4.2 KB
8. Recall.mp4	20.6 MB
8. Recall.vtt	3.7 KB
9. f1.mp4	12.7 MB
9. f1.vtt	2.3 KB
/5. Support Vector Machine (SVM)/
1. Support Vector Machine (SVM) Concepts.mp4	39.7 MB
1. Support Vector Machine (SVM) Concepts.vtt	8.2 KB
2. Linear SVM Classification.mp4	84.9 MB
2. Linear SVM Classification.vtt	12.4 KB
3. Polynomial Kernel.mp4	36.7 MB
3. Polynomial Kernel.vtt	5.6 KB
4. Radial Basis Function.mp4	73.5 MB
4. Radial Basis Function.vtt	9.0 KB
5. Support Vector Regression.mp4	62.6 MB
5. Support Vector Regression.vtt	9.5 KB
/6. Tree/
1. Introduction to Decision Tree.mp4	46.0 MB
1. Introduction to Decision Tree.vtt	8.1 KB
2. Training and Visualizing a Decision Tree.mp4	53.9 MB
2. Training and Visualizing a Decision Tree.vtt	7.2 KB
3. Visualizing Boundary.mp4	57.4 MB
3. Visualizing Boundary.vtt	9.0 KB
4. Tree Regression, Regularization and Over Fitting.mp4	42.0 MB
4. Tree Regression, Regularization and Over Fitting.vtt	5.4 KB
5. End to End Modeling.mp4	37.3 MB
5. End to End Modeling.vtt	5.5 KB
6. Project HR.mp4	186.5 MB
6. Project HR.vtt	28.8 KB
7. Project HR with Google Colab.mp4	69.8 MB
7. Project HR with Google Colab.vtt	11.7 KB
/7. Ensemble Machine Learning/
1. Ensemble Learning Methods Introduction.mp4	39.0 MB
1. Ensemble Learning Methods Introduction.vtt	5.7 KB
10. Ensemble of ensembles Part 2.mp4	39.7 MB
10. Ensemble of ensembles Part 2.vtt	5.9 KB
2. Bagging.mp4	173.5 MB
2. Bagging.vtt	21.6 KB
3. Random Forests and Extra-Trees.mp4	84.2 MB
3. Random Forests and Extra-Trees.vtt	11.3 KB
4. AdaBoost.mp4	52.3 MB
4. AdaBoost.vtt	8.1 KB
5. Gradient Boosting Machine.mp4	23.0 MB
5. Gradient Boosting Machine.vtt	3.7 KB
6. XGBoost Installation.mp4	23.3 MB
6. XGBoost Installation.vtt	2.9 KB
7. XGBoost.mp4	36.8 MB
7. XGBoost.vtt	5.2 KB
8. Project HR - Human Resources Analytics.mp4	62.1 MB
8. Project HR - Human Resources Analytics.vtt	9.7 KB
9. Ensemble of Ensembles Part 1.mp4	48.7 MB
9. Ensemble of Ensembles Part 1.vtt	7.5 KB
/8. k-Nearest Neighbours (kNN)/
1. kNN Introduction.mp4	66.0 MB
1. kNN Introduction.vtt	11.3 KB
2. Project Cancer Detection.mp4	79.4 MB
2. Project Cancer Detection.vtt	10.2 KB
3. Addition Materials.html	0.3 KB
4. Project Cancer Detection Part 1.mp4	51.8 MB
4. Project Cancer Detection Part 1.vtt	22.6 KB
4.1 0805.zip.zip	41.7 KB
/9. Unsupervised Learning Dimensionality Reduction/
1. Dimensionality Reduction Concept.mp4	32.9 MB
1. Dimensionality Reduction Concept.vtt	5.4 KB
2. PCA Introduction.mp4	51.4 MB
2. PCA Introduction.vtt	8.4 KB
3. Project Wine.mp4	50.2 MB
3. Project Wine.vtt	7.2 KB
4. Kernel PCA.mp4	38.4 MB
4. Kernel PCA.vtt	6.2 KB
5. Kernel PCA Demo.mp4	22.5 MB
5. Kernel PCA Demo.vtt	3.7 KB
6. LDA vs PCA.mp4	35.8 MB
6. LDA vs PCA.vtt	6.0 KB
7. Project Abalone.mp4	32.2 MB
7. Project Abalone.vtt	4.4 KB
Total files 221