[FreeTutorials.Us] Udemy - A-Z Machine Learning using Azure Machine Learning (AzureML)

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[FreeTutorials.Us] Udemy - A-Z Machine Learning using Azure Machine Learning (AzureML)

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文件列表

  • 7. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.mp4 95.5 MB
  • 12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.mp4 91.7 MB
  • 12. Text Analytics and Natural Language Processing/4. Feature Hashing.mp4 78.8 MB
  • 1. Basics of Machine Learning/4. Why Machine Learning is the Future.mp4 72.1 MB
  • 10. Feature Selection - Select a..highest impact/9. [Hands On] - Fisher Based LDA - Experiment.mp4 64.1 MB
  • 12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.mp4 57.3 MB
  • 4. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4 54.7 MB
  • 3. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.mp4 53.5 MB
  • 13. Thank You and Bonus Lecture/1. Way Forward.mp4 52.4 MB
  • 12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.mp4 52.4 MB
  • 10. Feature Selection - Select a..highest impact/2. Pearson Correlation Coefficient.mp4 49.5 MB
  • 12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.mp4 47.0 MB
  • 3. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4 40.8 MB
  • 11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.mp4 37.9 MB
  • 3. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4 37.2 MB
  • 4. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4 36.8 MB
  • 11. Recommendation System/1. What is a Recommendation System.mp4 36.7 MB
  • 8. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.mp4 32.4 MB
  • 4. Classification/1. Logistic Regression - What is Logistic Regression.mp4 32.2 MB
  • 4. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4 30.8 MB
  • 7. Regression Analysis/5. Gradient Descent.mp4 29.0 MB
  • 3. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4 27.7 MB
  • 4. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4 26.4 MB
  • 10. Feature Selection - Select a..highest impact/8. Fisher Based LDA - Intuition.mp4 25.3 MB
  • 2. Getting Started with Azure ML/2. What is Azure ML and high level architecture..mp4 24.3 MB
  • 8. Clustering/1. What is Cluster Analysis.mp4 23.5 MB
  • 3. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.mp4 23.1 MB
  • 5. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp4 23.0 MB
  • 4. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4 20.6 MB
  • 4. Classification/3. Logistic Regression - Understand Parameters and Their Impact.mp4 20.5 MB
  • 1. Basics of Machine Learning/3. Important Message About Udemy Reviews.mp4 20.2 MB
  • 1. Basics of Machine Learning/1. What You Will Learn in This Section.mp4 20.1 MB
  • 1. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4 20.0 MB
  • 4. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp4 19.5 MB
  • 1. Basics of Machine Learning/5. What is Machine Learning.mp4 19.4 MB
  • 8. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4 19.3 MB
  • 9. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.mp4 18.5 MB
  • 11. Recommendation System/6. Understanding the Matchbox Recommendation Results.mp4 18.3 MB
  • 7. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp4 18.1 MB
  • 6. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.mp4 17.4 MB
  • 9. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.mp4 16.7 MB
  • 9. Data Processing - Solving Data Processing Challenges/9. [Hands On] - SMOTE.mp4 16.3 MB
  • 9. Data Processing - Solving Data Processing Challenges/15. [Hands On] - Join Data - Experiment.mp4 15.8 MB
  • 11. Recommendation System/2. Data Preparation using Recommender Split.mp4 15.6 MB
  • 4. Classification/14. SVM - What is Support Vector Machine.mp4 15.6 MB
  • 11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.mp4 15.3 MB
  • 4. Classification/7. Decision Tree - What is Decision Tree.mp4 15.0 MB
  • 9. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.mp4 14.9 MB
  • 7. Regression Analysis/1. What is Linear Regression.mp4 14.7 MB
  • 4. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.mp4 14.5 MB
  • 4. Classification/5. Logistic Regression - Model Selection and Impact Analysis.mp4 14.4 MB
  • 1. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.mp4 14.3 MB
  • 2. Getting Started with Azure ML/1. What You Will Learn in This Section.mp4 14.2 MB
  • 2. Getting Started with Azure ML/3. Creating a Free Azure ML Account.mp4 14.1 MB
  • 1. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4 13.9 MB
  • 2. Getting Started with Azure ML/5. Azure ML Experiment Workflow.mp4 13.9 MB
  • 10. Feature Selection - Select a..highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp4 13.8 MB
  • 3. Data Processing/3. [Hands On] - Data Input-Output - Import Data.mp4 13.8 MB
  • 9. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.mp4 13.7 MB
  • 4. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp4 13.5 MB
  • 7. Regression Analysis/2. Regression Analysis - Common Metrics.mp4 13.2 MB
  • 7. Regression Analysis/8. Decision Tree - What is Regression Tree.mp4 12.8 MB
  • 2. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.mp4 12.8 MB
  • 4. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp4 12.7 MB
  • 9. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.mp4 12.3 MB
  • 9. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.mp4 12.1 MB
  • 2. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.mp4 11.8 MB
  • 11. Recommendation System/4. How to Score the Matchbox Recommender.mp4 11.5 MB
  • 7. Regression Analysis/7. [Hands On] - Experiment Online Gradient.mp4 11.4 MB
  • 9. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.mp4 11.3 MB
  • 9. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.mp4 11.0 MB
  • 7. Regression Analysis/4. [Hands On] - Linear Regression - R Squared.mp4 10.8 MB
  • 6. Deploy Webservice/2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp4 9.6 MB
  • 10. Feature Selection - Select a..highest impact/3. Chi Square Test of Independence.mp4 8.7 MB
  • 9. Data Processing - Solving Data Processing Challenges/3. [Hands On] - Summarize Data - Experiment.mp4 8.5 MB
  • 10. Feature Selection - Select a..highest impact/1. Feature Selection - Section Introduction.mp4 8.1 MB
  • 9. Data Processing - Solving Data Processing Challenges/13. [Hands On] - Principal Component Analysis.mp4 7.8 MB
  • 7. Regression Analysis/6. Linear Regression Online Gradient Descent.mp4 7.0 MB
  • 10. Feature Selection - Select a..highest impact/4. Kendall Correlation Coefficient.mp4 7.0 MB
  • 10. Feature Selection - Select a..highest impact/7. [Hands On] - Filter Based Selection - AzureML Experiment.mp4 6.7 MB
  • 10. Feature Selection - Select a..highest impact/5. Spearman's Rank Correlation.mp4 6.7 MB
  • 9. Data Processing - Solving Data Processing Challenges/11. [Hands On] - Data Normalization.mp4 6.2 MB
  • 4. Classification/11. Decision Forest - Parameters Explained.mp4 6.1 MB
  • 6. Deploy Webservice/1. Azure ML Webservice - Prepare the experiment for webservice.mp4 5.8 MB
  • 9. Data Processing - Solving Data Processing Challenges/1. Section Introduction.mp4 5.7 MB
  • 9. Data Processing - Solving Data Processing Challenges/10. Data Normalization - Scale and Reduce.mp4 5.6 MB
  • 4. Classification/10.1 Bank Telemarketing.csv.csv 4.9 MB
  • 7. Regression Analysis/9. Decision Tree - What is Boosted Decision Tree Regression.mp4 4.5 MB
  • 1. Basics of Machine Learning/2.9 Section 04 - Classification - 002 - Decision Tree.pdf.pdf 3.6 MB
  • 1. Basics of Machine Learning/2.11 Section 11 - Recommendation System.pdf.pdf 3.2 MB
  • 1. Basics of Machine Learning/2.12 Section 10 - Feature Selection.pdf.pdf 3.1 MB
  • 1. Basics of Machine Learning/2.14 Section 09 - Data Processing.pdf.pdf 3.0 MB
  • 1. Basics of Machine Learning/2.5 Section 07 - Regression.pdf.pdf 3.0 MB
  • 1. Basics of Machine Learning/2.4 Section 02 - Getting Started with AzureML.pdf.pdf 2.8 MB
  • 2. Getting Started with Azure ML/6.1 ml_studio_overview_v1.1.pdf.pdf 2.4 MB
  • 1. Basics of Machine Learning/2.3 Section - Text Analytics.pdf.pdf 2.1 MB
  • 1. Basics of Machine Learning/2.1 Section 01 - Basics of Machine Learning.pdf.pdf 1.9 MB
  • 1. Basics of Machine Learning/2.7 Section 08 - Clustering.pdf.pdf 1.6 MB
  • 1. Basics of Machine Learning/2.6 Section 04 - Classification - 001 - Logistic Regression.pdf.pdf 1.5 MB
  • 1. Basics of Machine Learning/2.10 Section 05 - Tune Hyperparameter.pdf.pdf 1.2 MB
  • 1. Basics of Machine Learning/2.8 Section 04 - Classification - 003 - SVM.pdf.pdf 1.2 MB
  • 1. Basics of Machine Learning/2.13 Section 03 - Data Pre-processing.pdf.pdf 1.1 MB
  • 1. Basics of Machine Learning/2.2 Section 06 - Deploy Webservice.pdf.pdf 719.3 kB
  • 2. Getting Started with Azure ML/6.2 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf.pdf 413.8 kB
  • 13. Thank You and Bonus Lecture/1.1 Links for datasets.pdf.pdf 267.7 kB
  • 10. Feature Selection - Select a..highest impact/9.1 Wine-Low-Medium-High.csv.csv 97.6 kB
  • 3. Data Processing/5.1 Wine Quality Dataset.csv.csv 85.7 kB
  • 4. Classification/6.1 winequality-red.csv.csv 85.7 kB
  • 12. Text Analytics and Natural Language Processing/5.1 two-class complaints modified.txt.txt 48.5 kB
  • 4. Classification/2.1 Loan Approval Prediction.csv.csv 38.0 kB
  • 9. Data Processing - Solving Data Processing Challenges/7.1 MICE Loan Dataset.csv.csv 38.0 kB
  • 4. Classification/4.1 004 - Logistic Regression - Understanding the results.xlsx.xlsx 24.5 kB
  • 4. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.srt 22.4 kB
  • 4. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.vtt 20.2 kB
  • 3. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.srt 18.4 kB
  • 3. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.vtt 16.6 kB
  • 3. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.srt 16.5 kB
  • 11. Recommendation System/1. What is a Recommendation System.srt 16.5 kB
  • 12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.srt 15.6 kB
  • 11. Recommendation System/1. What is a Recommendation System.vtt 14.9 kB
  • 12. Text Analytics and Natural Language Processing/4. Feature Hashing.srt 14.9 kB
  • 3. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.vtt 14.9 kB
  • 4. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.srt 14.3 kB
  • 12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.vtt 13.5 kB
  • 8. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.srt 13.5 kB
  • 4. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.srt 13.4 kB
  • 12. Text Analytics and Natural Language Processing/4. Feature Hashing.vtt 13.0 kB
  • 11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.srt 12.9 kB
  • 4. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.vtt 12.8 kB
  • 4. Classification/3. Logistic Regression - Understand Parameters and Their Impact.srt 12.8 kB
  • 8. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.vtt 12.2 kB
  • 4. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.vtt 12.1 kB
  • 3. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.srt 11.8 kB
  • 11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.vtt 11.6 kB
  • 7. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.srt 11.5 kB
  • 4. Classification/3. Logistic Regression - Understand Parameters and Their Impact.vtt 11.5 kB
  • 12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.srt 11.3 kB
  • 8. Clustering/1. What is Cluster Analysis.srt 11.0 kB
  • 1. Basics of Machine Learning/5. What is Machine Learning.srt 11.0 kB
  • 3. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.vtt 10.6 kB
  • 1. Basics of Machine Learning/4. Why Machine Learning is the Future.srt 10.4 kB
  • 7. Regression Analysis/5. Gradient Descent.srt 10.3 kB
  • 1. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.srt 10.2 kB
  • 4. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.srt 10.2 kB
  • 8. Clustering/1. What is Cluster Analysis.vtt 10.0 kB
  • 1. Basics of Machine Learning/5. What is Machine Learning.vtt 10.0 kB
  • 7. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.vtt 9.9 kB
  • 12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.vtt 9.8 kB
  • 5. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.srt 9.8 kB
  • 1. Basics of Machine Learning/4. Why Machine Learning is the Future.vtt 9.4 kB
  • 1. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.vtt 9.4 kB
  • 7. Regression Analysis/5. Gradient Descent.vtt 9.3 kB
  • 3. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.srt 9.2 kB
  • 4. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.vtt 9.2 kB
  • 5. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.vtt 8.9 kB
  • 12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.srt 8.8 kB
  • 4. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.srt 8.6 kB
  • 12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.srt 8.6 kB
  • 1. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.srt 8.5 kB
  • 3. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.vtt 8.3 kB
  • 9. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.srt 8.2 kB
  • 11. Recommendation System/2. Data Preparation using Recommender Split.srt 8.2 kB
  • 11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.srt 8.2 kB
  • 11. Recommendation System/6. Understanding the Matchbox Recommendation Results.srt 8.2 kB
  • 3. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.srt 8.1 kB
  • 1. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.srt 8.1 kB
  • 4. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.srt 8.1 kB
  • 10. Feature Selection - Select a..highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.srt 8.0 kB
  • 4. Classification/7. Decision Tree - What is Decision Tree.srt 8.0 kB
  • 12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.vtt 7.7 kB
  • 4. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.vtt 7.7 kB
  • 10. Feature Selection - Select a..highest impact/2. Pearson Correlation Coefficient.srt 7.7 kB
  • 1. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.vtt 7.6 kB
  • 12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.vtt 7.6 kB
  • 2. Getting Started with Azure ML/5. Azure ML Experiment Workflow.srt 7.6 kB
  • 8. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.srt 7.5 kB
  • 11. Recommendation System/2. Data Preparation using Recommender Split.vtt 7.5 kB
  • 4. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.srt 7.5 kB
  • 9. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.vtt 7.4 kB
  • 11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.vtt 7.4 kB
  • 11. Recommendation System/6. Understanding the Matchbox Recommendation Results.vtt 7.4 kB
  • 9. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.srt 7.4 kB
  • 3. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.vtt 7.3 kB
  • 1. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.vtt 7.3 kB
  • 10. Feature Selection - Select a..highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.vtt 7.2 kB
  • 4. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.vtt 7.2 kB
  • 4. Classification/7. Decision Tree - What is Decision Tree.vtt 7.2 kB
  • 13. Thank You and Bonus Lecture/2. Bonus Lecture.html 7.1 kB
  • 10. Feature Selection - Select a..highest impact/1. Feature Selection - Section Introduction.srt 7.0 kB
  • 9. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.srt 7.0 kB
  • 9. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.srt 6.9 kB
  • 6. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.srt 6.9 kB
  • 2. Getting Started with Azure ML/5. Azure ML Experiment Workflow.vtt 6.9 kB
  • 8. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.vtt 6.8 kB
  • 10. Feature Selection - Select a..highest impact/2. Pearson Correlation Coefficient.vtt 6.7 kB
  • 4. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.vtt 6.7 kB
  • 9. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.vtt 6.6 kB
  • 10. Feature Selection - Select a..highest impact/9. [Hands On] - Fisher Based LDA - Experiment.srt 6.6 kB
  • 4. Classification/1. Logistic Regression - What is Logistic Regression.srt 6.6 kB
  • 9. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.srt 6.6 kB
  • 2. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.srt 6.6 kB
  • 3. Data Processing/3. [Hands On] - Data Input-Output - Import Data.srt 6.5 kB
  • 7. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.srt 6.4 kB
  • 10. Feature Selection - Select a..highest impact/1. Feature Selection - Section Introduction.vtt 6.4 kB
  • 9. Data Processing - Solving Data Processing Challenges/9.1 LoanSMOTE.csv.csv 6.3 kB
  • 9. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.srt 6.3 kB
  • 9. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.srt 6.3 kB
  • 7. Regression Analysis/2. Regression Analysis - Common Metrics.srt 6.3 kB
  • 9. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.vtt 6.3 kB
  • 9. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.vtt 6.2 kB
  • 7. Regression Analysis/8. Decision Tree - What is Regression Tree.srt 6.2 kB
  • 6. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.vtt 6.2 kB
  • 9. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.srt 6.2 kB
  • 10. Feature Selection - Select a..highest impact/3. Chi Square Test of Independence.srt 6.2 kB
  • 4. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.srt 6.1 kB
  • 9. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.vtt 6.0 kB
  • 2. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.vtt 6.0 kB
  • 4. Classification/1. Logistic Regression - What is Logistic Regression.vtt 6.0 kB
  • 10. Feature Selection - Select a..highest impact/9. [Hands On] - Fisher Based LDA - Experiment.vtt 6.0 kB
  • 7. Regression Analysis/1. What is Linear Regression.srt 5.9 kB
  • 3. Data Processing/3. [Hands On] - Data Input-Output - Import Data.vtt 5.9 kB
  • 11. Recommendation System/4. How to Score the Matchbox Recommender.srt 5.9 kB
  • 7. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.vtt 5.8 kB
  • 9. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.vtt 5.7 kB
  • 4. Classification/5. Logistic Regression - Model Selection and Impact Analysis.srt 5.7 kB
  • 7. Regression Analysis/2. Regression Analysis - Common Metrics.vtt 5.7 kB
  • 9. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.vtt 5.7 kB
  • 9. Data Processing - Solving Data Processing Challenges/9. [Hands On] - SMOTE.srt 5.7 kB
  • 4. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.srt 5.6 kB
  • 10. Feature Selection - Select a..highest impact/8. Fisher Based LDA - Intuition.srt 5.6 kB
  • 7. Regression Analysis/8. Decision Tree - What is Regression Tree.vtt 5.6 kB
  • 9. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.vtt 5.6 kB
  • 10. Feature Selection - Select a..highest impact/3. Chi Square Test of Independence.vtt 5.6 kB
  • 13. Thank You and Bonus Lecture/1. Way Forward.srt 5.6 kB
  • 4. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.vtt 5.5 kB
  • 7. Regression Analysis/1. What is Linear Regression.vtt 5.4 kB
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