Section 1 : Basics of Machine Learning
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Lecture 1 | What You Will Learn in This Section | 00:02:03 Duration |
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Lecture 2 | Note on DP-100 Exam and New Studio | 00:05:07 Duration |
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Lecture 3 | The course slides as well as Data Files for all sections | |
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Lecture 4 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 5 | Why Machine Learning is the Future | 00:09:43 Duration |
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Lecture 6 | What is Machine Learning | 00:09:31 Duration |
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Lecture 7 | Understanding various aspects of data - Type, Variables, Category | 00:07:06 Duration |
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Lecture 8 | Common Machine Learning Terms - Probability, Mean, Mode, Median, Range | 00:07:41 Duration |
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Lecture 9 | Types of Machine Learning Models - Classification, Regression, Clustering etc | 00:10:02 Duration |
Section 2 : Getting Started with Azure ML
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Lecture 1 | What You Will Learn in This Section | 00:02:08 Duration |
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Lecture 2 | What is Azure ML and high level architecture | 00:03:59 Duration |
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Lecture 3 | Creating a Free Azure ML Account | 00:03:24 Duration |
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Lecture 4 | Azure ML Studio Overview and walk-through | 00:05:01 Duration |
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Lecture 5 | Azure ML Experiment Workflow | 00:07:20 Duration |
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Lecture 6 | Azure ML Cheat Sheet for Model Selection | 00:06:02 Duration |
Section 3 : Data Processing
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Lecture 1 | [Hands On] - Data Input-Output - Upload Data | 00:08:18 Duration |
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Lecture 2 | [Hands On] - Data Input-Output - Convert and Unpack | 00:08:53 Duration |
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Lecture 3 | [Hands On] - Data Input-Output - Import Data | 00:05:46 Duration |
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Lecture 4 | [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns | 00:11:34 Duration |
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Lecture 5 | [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata | 00:18:30 Duration |
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Lecture 6 | [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data | 00:16:56 Duration |
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Lecture 7 | Update to Lecture Sequence |
Section 4 : Classification
Section 5 : Hyperparameter Tuning
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Lecture 1 | [Hands On] - Tune Hyperparameter for Best Parameter Selection | 00:09:53 Duration |
Section 6 : Deploy Webservice
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Lecture 1 | Azure ML Webservice - Prepare the experiment for webservice | 00:02:22 Duration |
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Lecture 2 | [Hands On] - Deploy Machine Learning Model As a Web Service | 00:03:28 Duration |
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Lecture 3 | [Hands On] - Use the Web Service - Example of Excel | 00:06:38 Duration |
Section 7 : Regression Analysis
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Lecture 1 | What is Linear Regression | 00:06:19 Duration |
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Lecture 2 | Regression Analysis - Common Metrics | 00:06:27 Duration |
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Lecture 3 | [Hands On] - Linear Regression model using OLS | 00:11:05 Duration |
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Lecture 4 | [Hands On] - Linear Regression - R Squared | 00:04:26 Duration |
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Lecture 5 | Gradient Descent | 00:10:49 Duration |
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Lecture 6 | Linear Regression Online Gradient Descent | 00:02:12 Duration |
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Lecture 7 | [Hands On] - Experiment Online Gradient | 00:04:21 Duration |
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Lecture 8 | Decision Tree - What is Regression Tree | 00:06:42 Duration |
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Lecture 9 | Decision Tree - What is Boosted Decision Tree Regression | 00:02:00 Duration |
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Lecture 10 | [Hands On] - Decision Tree - Experiment Boosted Decision Tree | 00:07:01 Duration |
Section 8 : Clustering
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Lecture 1 | What is Cluster Analysis | 00:11:52 Duration |
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Lecture 2 | [Hands On] - Cluster Analysis Experiment 1 | 00:13:16 Duration |
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Lecture 3 | [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate | 00:08:04 Duration |
Section 9 : Data Processing - Solving Data Processing Challenges
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Lecture 1 | Section Introduction | 00:02:49 Duration |
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Lecture 2 | How to Summarize Data | 00:06:29 Duration |
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Lecture 3 | [Hands On] - Summarize Data - Experiment | 00:03:12 Duration |
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Lecture 4 | Outliers Treatment - Clip Values | 00:06:52 Duration |
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Lecture 5 | [Hands On] - Outliers Treatment - Clip Values | 00:07:51 Duration |
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Lecture 6 | Clean Missing Data with MICE | 00:07:19 Duration |
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Lecture 7 | [Hands On] - Clean Missing Data with MICE | 00:06:44 Duration |
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Lecture 8 | SMOTE - Create New Synthetic Observations | 00:08:34 Duration |
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Lecture 9 | [Hands On] - SMOTE | 00:05:50 Duration |
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Lecture 10 | Data Normalization - Scale and Reduce | 00:03:11 Duration |
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Lecture 11 | [Hands On] - Data Normalization | 00:02:32 Duration |
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Lecture 12 | PCA - What is PCA and Curse of Dimensionality | 00:06:24 Duration |
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Lecture 13 | [Hands On] - Principal Component Analysis | 00:03:24 Duration |
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Lecture 14 | Join Data - Join Multiple Datasets based on common keys | 00:06:03 Duration |
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Lecture 15 | [Hands On] - Join Data - Experiment | 00:02:43 Duration |
Section 10 : Feature Selection - Select a subset of Variables or features with highest impact
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Lecture 1 | Feature Selection - Section Introduction | |
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Lecture 2 | Pearson Correlation Coefficient | 00:07:12 Duration |
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Lecture 3 | Chi Square Test of Independence | 00:05:34 Duration |
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Lecture 4 | Kendall Correlation Coefficient | 00:04:11 Duration |
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Lecture 5 | Spearman's Rank Correlation | 00:03:43 Duration |
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Lecture 6 | [Hands On] - Comparison Experiment for Correlation Coefficients | 00:07:40 Duration |
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Lecture 7 | [Hands On] - Filter Based Selection - AzureML Experiment | 00:03:33 Duration |
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Lecture 8 | Fisher Based LDA - Intuition | 00:04:43 Duration |
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Lecture 9 | [Hands On] - Fisher Based LDA - Experiment | 00:05:46 Duration |
Section 11 : Recommendation System
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Lecture 1 | What is a Recommendation System | 00:16:57 Duration |
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Lecture 2 | Data Preparation using Recommender Split | 00:08:34 Duration |
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Lecture 3 | What is Matchbox Recommender and Train Matchbox Recommender | |
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Lecture 4 | How to Score the Matchbox Recommender | 00:05:43 Duration |
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Lecture 5 | [Hands On] - Restaurant Recommendation Experiment | 00:13:36 Duration |
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Lecture 6 | Understanding the Matchbox Recommendation Results | 00:08:58 Duration |
Section 12 : Text Analytics and Natural Language Processing
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Lecture 1 | What is Text Analytics or Natural Language Processing | 00:08:05 Duration |
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Lecture 2 | Text Pre-Processing | 00:14:06 Duration |
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Lecture 3 | Bag Of Words and N-Gram Models for Text features | 00:08:25 Duration |
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Lecture 4 | Feature Hashing | 00:14:48 Duration |
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Lecture 5 | Note for the next Hands On | |
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Lecture 6 | [Hands On] - Classify Customer Complaints using Text Analytics | 00:10:03 Duration |
Section 13 : ------- DP - 100 Certification Exam ---------
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Lecture 1 | DP-100 Exam Curriculum | 00:09:55 Duration |
Section 14 : Azure Machine Learning with Studio Designer
Section 15 : DesignerClassic Studio Vs Pandas and Scikit-learn
Section 16 : Azure Machine Learning with AzureML SDK
Section 17 : Azure AutoML
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Lecture 1 | To be Added |
Section 18 : Azure Hyperdrive
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Lecture 1 | To be Added |
Section 19 : Python Crash Course
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Lecture 1 | An Important Note | |
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Lecture 2 | Install Anaconda | 00:05:26 Duration |
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Lecture 3 | Hello World and Know your environment | 00:05:38 Duration |
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Lecture 4 | Variable Types in Python | 00:09:20 Duration |
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Lecture 5 | Conditional Statements in Python | 00:06:03 Duration |
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Lecture 6 | Python Loops explained | 00:02:40 Duration |
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Lecture 7 | While Loops in Python | 00:05:36 Duration |
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Lecture 8 | For Loop in Python | 00:05:17 Duration |
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Lecture 9 | Python Lists | 00:01:58 Duration |
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Lecture 10 | Python Lists - Operations Part 1 | 00:04:09 Duration |
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Lecture 11 | Python Lists - Operations Part 2 | 00:02:33 Duration |
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Lecture 12 | Multidimensional Lists in Python | 00:04:32 Duration |
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Lecture 13 | Slicing a multidimensional list | 00:05:56 Duration |
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Lecture 14 | Python Tuples | 00:03:47 Duration |
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Lecture 15 | Python Dictionary | 00:03:41 Duration |
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Lecture 16 | Python Dictionary Hands on Part 1 | 00:04:56 Duration |
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Lecture 17 | Python Dictionary Hands on Part 2 | 00:04:22 Duration |
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Lecture 18 | Python Functions | 00:05:08 Duration |
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Lecture 19 | Python Functions - Hands on | 00:05:37 Duration |
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Lecture 20 | Global Vs Local Variables in Python | 00:08:39 Duration |
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Lecture 21 | Types of Function Arguments | 00:04:24 Duration |
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Lecture 22 | Function Arguments - Required Arguments | 00:07:49 Duration |
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Lecture 23 | Function Arguments - Default Arguments | 00:05:56 Duration |
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Lecture 24 | Function Arguments - Keyword Arguments | 00:07:50 Duration |
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Lecture 25 | Object Oriented Programming | 00:11:33 Duration |
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Lecture 26 | Define a Class and Create an Object | 00:14:54 Duration |
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Lecture 27 | Initialize the Class Attributes using __init__ | 00:08:56 Duration |
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Lecture 28 | Packages and Modules in Python | 00:05:59 Duration |
Section 20 : Azure Fundamentals
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Lecture 1 | What is Cloud Computing | 00:08:03 Duration |
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Lecture 2 | What is Azure | 00:04:11 Duration |
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Lecture 3 | Azure Basic Terms and Concepts | 00:05:09 Duration |
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Lecture 4 | Azure Storage and Data Resource | 00:09:34 Duration |
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Lecture 5 | Azure Storage hands on | 00:12:20 Duration |
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Lecture 6 | Azure ComputeVirtual Machines | 00:04:18 Duration |
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Lecture 7 | Dockers and Azure Container Registry | 00:05:47 Duration |