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DP-100: Designing and Implementing a Data Science Solution on Azure

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112 Students Enrolled 80 Lectures 09:42:32 Hours

Curriculum For This Course

Basics of Machine Learning

6Lectures

Time 00:46:51

Getting Started with Azure ML

6Lectures

Time 00:26:48

Data Processing

6Lectures

Time 01:09:56

Classification

15Lectures

Time 02:14:36

Hyperparameter Tuning

1Lectures

Time 00:09:53

Deploy Webservice

3Lectures

Time 00:12:28

Regression Analysis

10Lectures

Time 01:01:07

Clustering

3Lectures

Time 00:33:11

Data Processing - Solving Data Processing Challenges

15Lectures

Time 01:19:50

Feature Selection - Select a subset of Variables or features with highest impact

9Lectures

Time 00:45:33

Recommendation System

6Lectures

Time 01:02:19

Basics of Machine Learning
	1. What You Will Learn in This Section	02:02
	2. Why Machine Learning is the Future?	10:30
	3. What is Machine Learning?	09:31
	4. Understanding various aspects of data - Type, Variables, Category	07:06
	5. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range	07:41
	6. Types of Machine Learning Models - Classification, Regression, Clustering etc	10:02

Getting Started with Azure ML
	1. What You Will Learn in This Section?	02:08
	2. What is Azure ML and high level architecture.	03:59
	3. Creating a Free Azure ML Account	02:21
	4. Azure ML Studio Overview and walk-through	05:01
	5. Azure ML Experiment Workflow	07:20
	6. Azure ML Cheat Sheet for Model Selection	06:01

Data Processing
	1. Data Input-Output - Upload Data	08:18
	2. Data Input-Output - Convert and Unpack	08:53
	3. Data Input-Output - Import Data	05:46
	4. Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns	11:34
	5. Data Transform - Apply SQL Transformation, Clean Missing Data, Edit Metadata	18:29
	6. Sample and Split Data - How to Partition or Sample, Train and Test Data	16:56

Classification
	1. Logistic Regression - What is Logistic Regression?	06:46
	2. Logistic Regression - Build Two-Class Loan Approval Prediction Model	22:09
	3. Logistic Regression - Understand Parameters and Their Impact	11:19
	4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score	13:17
	5. Logistic Regression - Model Selection and Impact Analysis	05:50
	6. Logistic Regression - Build Multi-Class Wine Quality Prediction Model	08:13
	7. Decision Tree - What is Decision Tree?	07:35
	8. Decision Tree - Ensemble Learning - Bagging and Boosting	07:05
	9. Decision Tree - Parameters - Two Class Boosted Decision Tree	05:34
	10. Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction	10:43
	11. Decision Forest - Parameters Explained	03:37
	12. Two Class Decision Forest - Adult Census Income Prediction	14:43
	13. Decision Tree - Multi Class Decision Forest IRIS Data	08:14
	14. SVM - What is Support Vector Machine?	04:02
	15. SVM - Adult Census Income Prediction	05:32

Hyperparameter Tuning
	1. Tune Hyperparameter for Best Parameter Selection	09:53

Deploy Webservice
	1. Azure ML Webservice - Prepare the experiment for webservice	02:22
	2. Deploy Machine Learning Model As a Web Service	03:28
	3. Use the Web Service - Example of Excel	06:38

Regression Analysis
	1. What is Linear Regression?	06:19
	2. Regression Analysis - Common Metrics	06:27
	3. Linear Regression model using OLS	10:54
	4. Linear Regression - R Squared	04:26
	5. Gradient Descent	10:48
	6. Linear Regression: Online Gradient Descent	02:12
	7. LR - Experiment Online Gradient	04:21
	8. Decision Tree - What is Regression Tree?	06:41
	9. Decision Tree - What is Boosted Decision Tree Regression?	02:00
	10. Decision Tree - Experiment Boosted Decision Tree	07:01

Clustering
	1. What is Cluster Analysis?	11:52
	2. Cluster Analysis Experiment 1	13:16
	3. Cluster Analysis Experiment 2 - Score and Evaluate	08:04

Data Processing - Solving Data Processing Challenges
	1. Section Introduction	02:49
	2. How to Summarize Data?	06:29
	3. Summarize Data - Experiment	03:12
	4. Outliers Treatment - Clip Values	06:52
	5. Outliers Treatment - Clip Values Experiment	07:51
	6. Clean Missing Data with MICE	07:19
	7. Clean Missing Data with MICE - Experiment	06:44
	8. SMOTE - Create New Synthetic Observations	08:33
	9. SMOTE - Experiment	05:50
	10. Data Normalization - Scale and Reduce	03:11
	11. Data Normalization - Experiment	02:32
	12. PCA - What is PCA and Curse of Dimensionality?	06:24
	13. PCA - Experiment	03:24
	14. Join Data - Join Multiple Datasets based on common keys	06:03
	15. Join Data - Experiment	02:43

Feature Selection - Select a subset of Variables or features with highest impact
	1. Feature Selection - Section Introduction	05:48
	2. Pearson Correlation Coefficient	04:36
	3. Chi Square Test of Independence	05:34
	4. Kendall Correlation Coefficient	04:11
	5. Spearman's Rank Correlation	03:42
	6. Comparison Experiment for Correlation Coefficients	07:40
	7. Filter Based Selection - AzureML Experiment	03:33
	8. Fisher Based LDA - Intuition	04:43
	9. Fisher Based LDA - Experiment	05:46

Recommendation System
	1. What is a Recommendation System?	16:57
	2. Data Preparation using Recommender Split	08:34
	3. What is Matchbox Recommender and Train Matchbox Recommender	08:33
	4. How to Score the Matchbox Recommender?	05:43
	5. Restaurant Recommendation Experiment	13:36
	6. Understanding the Matchbox Recommendation Results	08:58

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DP-100: Designing and Implementing a Data Science Solution on Azure

Curriculum For This Course

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