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

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Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.

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Video Courses

Basics of Machine Learning

Lectures	Duration
1. What You Will Learn in This Section	2m 2s
2. Why Machine Learning is the Future?	10m 30s
3. What is Machine Learning?	9m 31s
4. Understanding various aspects of data - Type, Variables, Category	7m 6s
5. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range	7m 41s
6. Types of Machine Learning Models - Classification, Regression, Clustering etc	10m 2s

1. What You Will Learn in This Section

2m 2s

2. Why Machine Learning is the Future?

10m 30s

3. What is Machine Learning?

9m 31s

4. Understanding various aspects of data - Type, Variables, Category

7m 6s

5. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range

7m 41s

6. Types of Machine Learning Models - Classification, Regression, Clustering etc

10m 2s

Getting Started with Azure ML

Lectures	Duration
1. What You Will Learn in This Section?	2m 8s
2. What is Azure ML and high level architecture.	3m 59s
3. Creating a Free Azure ML Account	2m 21s
4. Azure ML Studio Overview and walk-through	5m 1s
5. Azure ML Experiment Workflow	7m 20s
6. Azure ML Cheat Sheet for Model Selection	6m 1s

1. What You Will Learn in This Section?

2m 8s

2. What is Azure ML and high level architecture.

3m 59s

3. Creating a Free Azure ML Account

2m 21s

4. Azure ML Studio Overview and walk-through

5m 1s

5. Azure ML Experiment Workflow

7m 20s

6. Azure ML Cheat Sheet for Model Selection

6m 1s

Data Processing

Lectures	Duration
1. Data Input-Output - Upload Data	8m 18s
2. Data Input-Output - Convert and Unpack	8m 53s
3. Data Input-Output - Import Data	5m 46s
4. Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns	11m 34s
5. Data Transform - Apply SQL Transformation, Clean Missing Data, Edit Metadata	18m 29s
6. Sample and Split Data - How to Partition or Sample, Train and Test Data	16m 56s

1. Data Input-Output - Upload Data

8m 18s

2. Data Input-Output - Convert and Unpack

8m 53s

3. Data Input-Output - Import Data

5m 46s

4. Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns

11m 34s

5. Data Transform - Apply SQL Transformation, Clean Missing Data, Edit Metadata

18m 29s

6. Sample and Split Data - How to Partition or Sample, Train and Test Data

16m 56s

Classification

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

1. Logistic Regression - What is Logistic Regression?

6m 46s

2. Logistic Regression - Build Two-Class Loan Approval Prediction Model

22m 9s

3. Logistic Regression - Understand Parameters and Their Impact

11m 19s

4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score

13m 17s

5. Logistic Regression - Model Selection and Impact Analysis

5m 50s

6. Logistic Regression - Build Multi-Class Wine Quality Prediction Model

8m 13s

7. Decision Tree - What is Decision Tree?

7m 35s

8. Decision Tree - Ensemble Learning - Bagging and Boosting

7m 5s

9. Decision Tree - Parameters - Two Class Boosted Decision Tree

5m 34s

10. Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction

10m 43s

11. Decision Forest - Parameters Explained

3m 37s

12. Two Class Decision Forest - Adult Census Income Prediction

14m 43s

13. Decision Tree - Multi Class Decision Forest IRIS Data

8m 14s

14. SVM - What is Support Vector Machine?

4m 2s

15. SVM - Adult Census Income Prediction

5m 32s

Hyperparameter Tuning

Lectures	Duration
1. Tune Hyperparameter for Best Parameter Selection	9m 53s

1. Tune Hyperparameter for Best Parameter Selection

9m 53s

Deploy Webservice

Lectures	Duration
1. Azure ML Webservice - Prepare the experiment for webservice	2m 22s
2. Deploy Machine Learning Model As a Web Service	3m 28s
3. Use the Web Service - Example of Excel	6m 38s

1. Azure ML Webservice - Prepare the experiment for webservice

2m 22s

2. Deploy Machine Learning Model As a Web Service

3m 28s

3. Use the Web Service - Example of Excel

6m 38s

Regression Analysis

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

1. What is Linear Regression?

6m 19s

2. Regression Analysis - Common Metrics

6m 27s

3. Linear Regression model using OLS

10m 54s

4. Linear Regression - R Squared

4m 26s

5. Gradient Descent

10m 48s

6. Linear Regression: Online Gradient Descent

2m 12s

7. LR - Experiment Online Gradient

4m 21s

8. Decision Tree - What is Regression Tree?

6m 41s

9. Decision Tree - What is Boosted Decision Tree Regression?

10. Decision Tree - Experiment Boosted Decision Tree

7m 1s

Clustering

Lectures	Duration
1. What is Cluster Analysis?	11m 52s
2. Cluster Analysis Experiment 1	13m 16s
3. Cluster Analysis Experiment 2 - Score and Evaluate	8m 4s

1. What is Cluster Analysis?

11m 52s

2. Cluster Analysis Experiment 1

13m 16s

3. Cluster Analysis Experiment 2 - Score and Evaluate

8m 4s

Data Processing - Solving Data Processing Challenges

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

1. Section Introduction

2m 49s

2. How to Summarize Data?

6m 29s

3. Summarize Data - Experiment

3m 12s

4. Outliers Treatment - Clip Values

6m 52s

5. Outliers Treatment - Clip Values Experiment

7m 51s

6. Clean Missing Data with MICE

7m 19s

7. Clean Missing Data with MICE - Experiment

6m 44s

8. SMOTE - Create New Synthetic Observations

8m 33s

9. SMOTE - Experiment

5m 50s

10. Data Normalization - Scale and Reduce

3m 11s

11. Data Normalization - Experiment

2m 32s

12. PCA - What is PCA and Curse of Dimensionality?

6m 24s

13. PCA - Experiment

3m 24s

14. Join Data - Join Multiple Datasets based on common keys

6m 3s

15. Join Data - Experiment

2m 43s

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

Lectures	Duration
1. Feature Selection - Section Introduction	5m 48s
2. Pearson Correlation Coefficient	4m 36s
3. Chi Square Test of Independence	5m 34s
4. Kendall Correlation Coefficient	4m 11s
5. Spearman's Rank Correlation	3m 42s
6. Comparison Experiment for Correlation Coefficients	7m 40s
7. Filter Based Selection - AzureML Experiment	3m 33s
8. Fisher Based LDA - Intuition	4m 43s
9. Fisher Based LDA - Experiment	5m 46s

1. Feature Selection - Section Introduction

5m 48s

2. Pearson Correlation Coefficient

4m 36s

3. Chi Square Test of Independence

5m 34s

4. Kendall Correlation Coefficient

4m 11s

5. Spearman's Rank Correlation

3m 42s

6. Comparison Experiment for Correlation Coefficients

7m 40s

7. Filter Based Selection - AzureML Experiment

3m 33s

8. Fisher Based LDA - Intuition

4m 43s

9. Fisher Based LDA - Experiment

5m 46s

Recommendation System

Lectures	Duration
1. What is a Recommendation System?	16m 57s
2. Data Preparation using Recommender Split	8m 34s
3. What is Matchbox Recommender and Train Matchbox Recommender	8m 33s
4. How to Score the Matchbox Recommender?	5m 43s
5. Restaurant Recommendation Experiment	13m 36s
6. Understanding the Matchbox Recommendation Results	8m 58s

1. What is a Recommendation System?

16m 57s

2. Data Preparation using Recommender Split

8m 34s

3. What is Matchbox Recommender and Train Matchbox Recommender

8m 33s

4. How to Score the Matchbox Recommender?

5m 43s

5. Restaurant Recommendation Experiment

13m 36s

6. Understanding the Matchbox Recommendation Results

8m 58s