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

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Prepare for your Microsoft examination with our training course. The DP-100 course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft DP-100 test with flying colors.
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Curriculum For This Course
  • 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. 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. 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. Tune Hyperparameter for Best Parameter Selection 9m 53s
  • 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. 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 Cluster Analysis? 11m 52s
  • 2. Cluster Analysis Experiment 1 13m 16s
  • 3. Cluster Analysis Experiment 2 - Score and Evaluate 8m 4s
  • 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. 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. 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

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