Designing and Implementing a Data Science Solution on Azure Video Course

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

Your Microsoft DP-100 exam is just around the corner, right? So, it's high time to find an effective preparation tool! Our training course is what you really need! This is a series of videos led by the experienced IT instructors who will provide you with a detailed overview of the DP-100 certification test. Ace your Microsoft DP-100 at the first attempt and obtain the Designing and Implementing a Data Science Solution on Azure credential with ease.

# of Students
100
# of Lectures
80
Course Length
9 h
Course Rating
4.6
Price $32.99
Today $29.99

Curriculum for DP-100 Video Course

Basics of Machine Learning

  Video Name Time
  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
  Video Name Time
  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
  Video Name Time
  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
  Video Name Time
  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
  Video Name Time
  1. Tune Hyperparameter for Best Parameter Selection 09:53
  Video Name Time
  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
  Video Name Time
  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
  Video Name Time
  1. What is Cluster Analysis? 11:52
  2. Cluster Analysis Experiment 1 13:16
  3. Cluster Analysis Experiment 2 - Score and Evaluate 08:04
  Video Name Time
  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
  Video Name Time
  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
  Video Name Time
  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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