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

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.

What’s included

80 : Lectures
9h 42m 49s : Duration

$14.99 / $24.99

Basics of Machine Learning

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

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

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

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

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

Deploy Webservice

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

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

Clustering

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

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

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

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