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.
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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