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

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

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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