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种子名称： [FTUForum.com] [UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU]
文件类型： 视频
文件数目： 87个文件
文件大小： 1.85 GB
收录时间： 2020-6-16 13:53
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最近下载： 2024-7-2 22:29

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[FTUForum.com] [UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU].torrent

01. Basics of Machine Learning/1. What You Will Learn in This Section.mp418.92MB

01. Basics of Machine Learning/3. Important Message About Udemy Reviews.mp418.91MB

01. Basics of Machine Learning/4. Why Machine Learning is the Future.mp468.72MB

01. Basics of Machine Learning/5. What is Machine Learning.mp454.73MB

01. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.mp413.61MB

01. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp413.31MB

01. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp419.04MB

02. Getting Started with Azure ML/1. What You Will Learn in This Section.mp413.33MB

02. Getting Started with Azure ML/2. What is Azure ML and high level architecture..mp422.86MB

02. Getting Started with Azure ML/3. Creating a Free Azure ML Account.mp45.42MB

02. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.mp412.17MB

02. Getting Started with Azure ML/5. Azure ML Experiment Workflow.mp413.22MB

02. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.mp411.27MB

03. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.mp418.57MB

03. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.mp422.09MB

03. Data Processing/3. [Hands On] - Data Input-Output - Import Data.mp413.12MB

03. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp426.47MB

03. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp438.91MB

03. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp435.52MB

04. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp425.17MB

04. Classification/11. Decision Forest - Parameters Explained.mp45.79MB

04. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp435.1MB

04. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp418.57MB

04. Classification/14. SVM - What is Support Vector Machine.mp47.13MB

04. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.mp413.83MB

04. Classification/1. Logistic Regression - What is Logistic Regression.mp411.48MB

04. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp452.21MB

04. Classification/3. Logistic Regression - Understand Parameters and Their Impact.mp419.55MB

04. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp429.42MB

04. Classification/5. Logistic Regression - Model Selection and Impact Analysis.mp413.77MB

04. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp419.67MB

04. Classification/7. Decision Tree - What is Decision Tree.mp414.34MB

04. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp412.91MB

04. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp412.1MB

05. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp421.92MB

06. Deploy Webservice/1. Azure ML Webservice - Prepare the experiment for webservice.mp45.56MB

06. Deploy Webservice/2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp49.18MB

06. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.mp416.58MB

07. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp417.28MB

07. Regression Analysis/1. What is Linear Regression.mp414.03MB

07. Regression Analysis/2. Regression Analysis - Common Metrics.mp412.59MB

07. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.mp491.03MB

07. Regression Analysis/4. [Hands On] - Linear Regression - R Squared.mp410.33MB

07. Regression Analysis/5. Gradient Descent.mp427.66MB

07. Regression Analysis/6. Linear Regression Online Gradient Descent.mp46.71MB

07. Regression Analysis/7. [Hands On] - Experiment Online Gradient.mp410.85MB

07. Regression Analysis/8. Decision Tree - What is Regression Tree.mp412.24MB

07. Regression Analysis/9. Decision Tree - What is Boosted Decision Tree Regression.mp44.32MB

08. Clustering/1. What is Cluster Analysis.mp422.38MB

08. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.mp430.92MB

08. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp418.37MB

09. Data Processing - Solving Data Processing Challenges/10. Data Normalization - Scale and Reduce.mp45.33MB

09. Data Processing - Solving Data Processing Challenges/11. [Hands On] - Data Normalization.mp45.88MB

09. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.mp410.73MB

09. Data Processing - Solving Data Processing Challenges/13. [Hands On] - Principal Component Analysis.mp47.41MB

09. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.mp410.48MB

09. Data Processing - Solving Data Processing Challenges/15. [Hands On] - Join Data - Experiment.mp45.56MB

09. Data Processing - Solving Data Processing Challenges/1. Section Introduction.mp45.41MB

09. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.mp411.7MB

09. Data Processing - Solving Data Processing Challenges/3. [Hands On] - Summarize Data - Experiment.mp48.15MB

09. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.mp411.49MB

09. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.mp417.65MB

09. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.mp413.05MB

09. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.mp415.91MB

09. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.mp414.21MB

09. Data Processing - Solving Data Processing Challenges/9. [Hands On] - SMOTE.mp415.54MB

10. Feature Selection - Select a subset of Variables or features with highest impact/1. Feature Selection - Section Introduction.mp47.73MB

10. Feature Selection - Select a subset of Variables or features with highest impact/2. Pearson Correlation Coefficient.mp447.22MB

10. Feature Selection - Select a subset of Variables or features with highest impact/3. Chi Square Test of Independence.mp48.28MB

10. Feature Selection - Select a subset of Variables or features with highest impact/4. Kendall Correlation Coefficient.mp46.71MB

10. Feature Selection - Select a subset of Variables or features with highest impact/5. Spearman's Rank Correlation.mp46.37MB

10. Feature Selection - Select a subset of Variables or features with highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp413.19MB

10. Feature Selection - Select a subset of Variables or features with highest impact/7. [Hands On] - Filter Based Selection - AzureML Experiment.mp46.38MB

10. Feature Selection - Select a subset of Variables or features with highest impact/8. Fisher Based LDA - Intuition.mp424.08MB

10. Feature Selection - Select a subset of Variables or features with highest impact/9. [Hands On] - Fisher Based LDA - Experiment.mp461.14MB

11. Recommendation System/1. What is a Recommendation System.mp434.97MB

11. Recommendation System/2. Data Preparation using Recommender Split.mp414.92MB

11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.mp414.56MB

11. Recommendation System/4. How to Score the Matchbox Recommender.mp410.94MB

11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.mp436.18MB

11. Recommendation System/6. Understanding the Matchbox Recommendation Results.mp417.44MB

12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.mp440.7MB

12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.mp454.61MB

12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.mp449.96MB

12. Text Analytics and Natural Language Processing/4. Feature Hashing.mp475.17MB

12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.mp490.99MB

13. Thank You and Bonus Lecture/1. Way Forward.mp449.18MB