Video Course
BEST SELLER

DP-100: Designing and Implementing a Data Science Solution on Azure Certification Video Training Course

Looking for studying for DP-100: Designing and Implementing a Data Science Solution on Azure certification training course. Our DP-100 certification video training course training course is your number one assistant. Pass with our DP-100: Designing and Implementing a Data Science Solution on Azure certification training course on the first try and become a certified professional in no time.

  • 140 Students Enrolled
  • 80 Lectures
  • 9 Hours
  • 4.5 Rating
$38.49
$34.99

Curriculum For This 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

  • Getting Started with Azure ML

    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

  • Data Processing

    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

  • Classification

    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

  • Hyperparameter Tuning

    Video Name Time
    1. Tune Hyperparameter for Best Parameter Selection 09:53

  • Deploy Webservice

    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

  • Regression Analysis

    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

  • Clustering

    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

  • Data Processing - Solving Data Processing Challenges

    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

  • Feature Selection - Select a subset of Variables or features with highest impact

    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

  • Recommendation System

    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

Student Feedback

  1. 51%
  2. 49%
  3. 0%
  4. 0%
  5. 0%

Good Overall Rating

4.5

Add Comment

Rate Course *

Secure Code
Please enter security code exactly as shown

Log into your CertBolt Account

* Only Registered Members can Download Exam Questions & Answers

Create New CertBolt Account

or Login