Udemy - The Complete Machine Learning Course with Python

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Udemy - The Complete Machine Learning Course with Python (Size: 6.8 GB)

	1. Clustering.mp4	125.7 MB
	1. Clustering.vtt	18.7 KB
	1. Dimensionality Reduction Concept.mp4	31.4 MB
	1. Dimensionality Reduction Concept.vtt	5.3 KB
	1. Ensemble Learning Methods Introduction.mp4	37.2 MB
	1. Ensemble Learning Methods Introduction.vtt	5.6 KB
	1. Estimating Simple Function with Neural Networks.mp4	143.9 MB
	1. Estimating Simple Function with Neural Networks.vtt	24.4 KB
	1. Installing Applications and Creating Environment.mp4	38.4 MB
	1. Installing Applications and Creating Environment.vtt	6 KB
	1. Introduction to Decision Tree.mp4	43.9 MB
	1. Introduction to Decision Tree.vtt	7.9 KB
	1. Introduction to Neural Networks.mp4	13.8 MB
	1. Introduction to Neural Networks.vtt	2.5 KB
	1. Logistic Regression.mp4	119.6 MB
	1. Logistic Regression.vtt	23.5 KB
	1. Outline.mp4	63.7 MB
	1. Outline.vtt	4.1 KB
	1. Scikit-Learn.mp4	48.5 MB
	1. Scikit-Learn.vtt	10 KB
	1. Support Vector Machine (SVM) Concepts.mp4	37.9 MB
	1. Support Vector Machine (SVM) Concepts.vtt	8 KB
	1. What Does the Course Cover.mp4	54.4 MB
	1. What Does the Course Cover.vtt	3 KB
	1. kNN Introduction.mp4	62.9 MB
	1. kNN Introduction.vtt	11 KB
	10. Ensemble of ensembles Part 2.mp4	37.8 MB
	10. Ensemble of ensembles Part 2.vtt	5.7 KB
	10. Gradient Based Optimization.mp4	55 MB
	10. Gradient Based Optimization.vtt	12.6 KB
	10. Multiple Regression 2.mp4	91.2 MB
	10. Multiple Regression 2.vtt	13.8 KB
	10. Precision Recall Tradeoff.mp4	102 MB
	10. Precision Recall Tradeoff.vtt	20.8 KB
	10. Training Your CNN 1.mp4	124.9 MB
	10. Training Your CNN 1.vtt	15.2 KB
	11. Altering the Precision Recall Tradeoff.mp4	20.9 MB
	11. Altering the Precision Recall Tradeoff.vtt	3.5 KB
	11. Getting Started with Neural Network and Deep Learning Libraries.mp4	18.7 MB
	11. Getting Started with Neural Network and Deep Learning Libraries.vtt	5.1 KB
	11. Regularized Regression.mp4	44.3 MB
	11. Regularized Regression.vtt	7.8 KB
	11. Training Your CNN 2.mp4	128.5 MB
	11. Training Your CNN 2.vtt	22.4 KB
	12. Categories of Machine Learning.mp4	37.5 MB
	12. Categories of Machine Learning.vtt	11.2 KB
	12. Loading Previously Trained Model.mp4	11.2 MB
	12. Loading Previously Trained Model.vtt	1.6 KB
	12. Polynomial Regression.mp4	110.8 MB
	12. Polynomial Regression.vtt	19.7 KB
	12. ROC.mp4	52.2 MB
	12. ROC.vtt	7.6 KB
	13. Dealing with Non-linear Relationships.mp4	62.7 MB
	13. Dealing with Non-linear Relationships.vtt	10.3 KB
	13. Model Performance Comparison.mp4	79.8 MB
	13. Model Performance Comparison.vtt	10.7 KB
	13. Over and Under Fitting.mp4	70.1 MB
	13. Over and Under Fitting.vtt	16.7 KB
	14. Data Augmentation.mp4	28.5 MB
	14. Data Augmentation.vtt	3.3 KB
	14. Feature Importance.mp4	36.3 MB
	14. Feature Importance.vtt	5.4 KB
	14. Machine Learning Workflow.mp4	27.4 MB
	14. Machine Learning Workflow.vtt	5.3 KB
	15. Data Preprocessing.mp4	135.5 MB
	15. Data Preprocessing.vtt	25.5 KB
	15. Transfer Learning.mp4	97 MB
	15. Transfer Learning.vtt	12.1 KB
	16. Feature Extraction.mp4	111.1 MB
	16. Feature Extraction.vtt	12.9 KB
	16. Variance-Bias Trade Off.mp4	68.7 MB
	16. Variance-Bias Trade Off.vtt	13.7 KB
	17. Learning Curve.mp4	56.4 MB
	17. Learning Curve.vtt	10.2 KB
	17. State of the Art Tools.mp4	35.4 MB
	17. State of the Art Tools.vtt	6 KB
	18. Cross Validation.mp4	48 MB
	18. Cross Validation.vtt	9.7 KB
	19. CV Illustration.mp4	127.2 MB
	19. CV Illustration.vtt	19.9 KB
	2. Bagging.mp4	165.4 MB
	2. Bagging.vtt	21.1 KB
	2. Differences between Classical Programming and Machine Learning.mp4	20.9 MB
	2. Differences between Classical Programming and Machine Learning.vtt	4.9 KB
	2. EDA.mp4	151.7 MB
	2. EDA.vtt	22.4 KB
	2. Hello World.mp4	51.2 MB
	2. Hello World.vtt	12.5 KB
	2. How to Succeed in This Course.html	2.2 KB
	2. Introduction to Classification.mp4	42.1 MB
	2. Introduction to Classification.vtt	5.7 KB
	2. Linear SVM Classification.mp4	80.9 MB
	2. Linear SVM Classification.vtt	12.1 KB
	2. Neural Network Architecture.mp4	22.4 MB
	2. Neural Network Architecture.vtt	7.2 KB
	2. Neural Network Revision.mp4	43.8 MB
	2. Neural Network Revision.vtt	9.2 KB
	2. PCA Introduction.mp4	49 MB
	2. PCA Introduction.vtt	8.2 KB
	2. Project Cancer Detection.mp4	75.7 MB
	2. Project Cancer Detection.vtt	10 KB
	2. Training and Visualizing a Decision Tree.mp4	51.4 MB
	2. Training and Visualizing a Decision Tree.vtt	7 KB
	2. k_Means Clustering.mp4	57.7 MB
	2. k_Means Clustering.vtt	10 KB
	3. Addition Materials.html	307 B
	3. Correlation Analysis and Feature Selection.mp4	22.6 MB
	3. Correlation Analysis and Feature Selection.vtt	9.8 KB
	3. Iris Project 1 Working with Error Messages.mp4	89.8 MB
	3. Iris Project 1 Working with Error Messages.vtt	14.5 KB
	3. Learning Representations.mp4	77.2 MB
	3. Learning Representations.vtt	11.5 KB
	3. Motivational Example - Project MNIST.mp4	145 MB
	3. Motivational Example - Project MNIST.vtt	23.5 KB
	3. Motivational Example.mp4	66.2 MB
	3. Motivational Example.vtt	8.7 KB
	3. Polynomial Kernel.mp4	35 MB
	3. Polynomial Kernel.vtt	5.5 KB
	3. Project Files and Resources.html	1.7 KB
	3. Project Wine.mp4	47.9 MB
	3. Project Wine.vtt	7 KB
	3. Random Forests and Extra-Trees.mp4	80.3 MB
	3. Random Forests and Extra-Trees.vtt	11.1 KB
	3. Understanding MNIST.mp4	109 MB
	3. Understanding MNIST.vtt	16.4 KB
	3. Visualizing Boundary.mp4	54.7 MB
	3. Visualizing Boundary.vtt	8.8 KB
	3.1 0305.zip.zip	2.1 MB
	4. AdaBoost.mp4	49.8 MB
	4. AdaBoost.vtt	7.9 KB
	4. Binary Classification Problem.mp4	72.1 MB
	4. Binary Classification Problem.vtt	11.5 KB
	4. Correlation Analysis and Feature Selection.mp4	105.2 MB
	4. Correlation Analysis and Feature Selection.vtt	13.9 KB
	4. Iris Project 2 Reading CSV Data into Memory.mp4	64.6 MB
	4. Iris Project 2 Reading CSV Data into Memory.vtt	10 KB
	4. Kernel PCA.mp4	36.6 MB
	4. Kernel PCA.vtt	6.1 KB
	4. Project Cancer Detection Part 1.mp4	49.4 MB
	4. Project Cancer Detection Part 1.vtt	22.1 KB
	4. Radial Basis Function.mp4	70.1 MB
	4. Radial Basis Function.vtt	8.8 KB
	4. SGD.mp4	57.3 MB
	4. SGD.vtt	10.6 KB
	4. Tree Regression, Regularization and Over Fitting.mp4	40.1 MB
	4. Tree Regression, Regularization and Over Fitting.vtt	5.3 KB
	4. Visualizing CNN.mp4	141.9 MB
	4. Visualizing CNN.vtt	15.4 KB
	4. What is Deep Learning.mp4	155.6 MB
	4. What is Deep Learning.vtt	23.1 KB
	4.1 0805.zip.zip	40.8 KB
	5. End to End Modeling.mp4	35.6 MB
	5. End to End Modeling.vtt	5.3 KB
	5. Gradient Boosting Machine.mp4	22 MB
	5. Gradient Boosting Machine.vtt	3.6 KB
	5. Iris Project 3 Loading data from Seaborn.mp4	55.9 MB
	5. Iris Project 3 Loading data from Seaborn.vtt	9.9 KB
	5. Kernel PCA Demo.mp4	21.4 MB
	5. Kernel PCA Demo.vtt	3.6 KB
	5. Learning Neural Networks.mp4	40.6 MB
	5. Learning Neural Networks.vtt	11.4 KB
	5. Linear Regression with Scikit-Learn.mp4	77 MB
	5. Linear Regression with Scikit-Learn.vtt	14.9 KB
	5. Natural Language Processing - Binary Classification.mp4	76 MB
	5. Natural Language Processing - Binary Classification.vtt	11.7 KB
	5. Performance Measure and Stratified k-Fold.mp4	51.5 MB
	5. Performance Measure and Stratified k-Fold.vtt	8.1 KB
	5. Support Vector Regression.mp4	59.7 MB
	5. Support Vector Regression.vtt	9.3 KB
	5. Understanding CNN.mp4	30 MB
	5. Understanding CNN.vtt	6.7 KB
	6. Confusion Matrix.mp4	54.7 MB
	6. Confusion Matrix.vtt	11 KB
	6. Five Steps Machine Learning Process.mp4	77.3 MB
	6. Five Steps Machine Learning Process.vtt	9.2 KB
	6. Iris Project 4 Visualization.mp4	93.5 MB
	6. Iris Project 4 Visualization.vtt	11.5 KB
	6. LDA vs PCA.mp4	34.1 MB
	6. LDA vs PCA.vtt	5.9 KB
	6. Layer - Input.mp4	29.1 MB
	6. Layer - Input.vtt	6.2 KB
	6. Project HR.mp4	177.8 MB
	6. Project HR.vtt	28.1 KB
	6. Why Now.mp4	9.1 MB
	6. Why Now.vtt	3 KB
	6. XGBoost Installation.mp4	22.3 MB
	6. XGBoost Installation.vtt	2.8 KB
	7. Building Block Introduction.mp4	14.2 MB
	7. Building Block Introduction.vtt	5.1 KB
	7. Layer - Filter.mp4	84.4 MB
	7. Layer - Filter.vtt	18.5 KB
	7. Precision.mp4	23.6 MB
	7. Precision.vtt	4.1 KB
	7. Project Abalone.mp4	30.7 MB
	7. Project Abalone.vtt	4.3 KB
	7. Project HR with Google Colab.mp4	66.6 MB
	7. Project HR with Google Colab.vtt	11.4 KB
	7. Robust Regression.mp4	119.1 MB
	7. Robust Regression.vtt	20.1 KB
	7. XGBoost.mp4	35.1 MB
	7. XGBoost.vtt	5.1 KB
	8. Activation Function.mp4	32.3 MB
	8. Activation Function.vtt	6.9 KB
	8. Evaluate Regression Model Performance.mp4	99.7 MB
	8. Evaluate Regression Model Performance.vtt	17.9 KB
	8. Project HR - Human Resources Analytics.mp4	59.2 MB
	8. Project HR - Human Resources Analytics.vtt	9.5 KB
	8. Recall.mp4	19.6 MB
	8. Recall.vtt	3.7 KB
	8. Tensors.mp4	16.9 MB
	8. Tensors.vtt	4.3 KB
	9. Ensemble of Ensembles Part 1.mp4	46.4 MB
	9. Ensemble of Ensembles Part 1.vtt	7.3 KB
	9. Multiple Regression 1.mp4	125.5 MB
	9. Multiple Regression 1.vtt	22.5 KB
	9. Pooling, Flatten, Dense.mp4	88.1 MB
	9. Pooling, Flatten, Dense.vtt	12.5 KB
	9. Tensor Operations.mp4	88.8 MB
	9. Tensor Operations.vtt	18.9 KB
	9. f1.mp4	12.1 MB
	9. f1.vtt	2.3 KB
▲ 221 total files

Description

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Created by Codestars by Rob Percival, Anthony NG, Rob Percival
Last updated 11/2019
English
What you'll learn

Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
Solve any problem in your business, job or personal life with powerful Machine Learning models
Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc

For More Courses Visit: https://FreeAllCourse.com

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Udemy - The Complete Machine Learning Course with Python

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