Section 1 : Basics of Machine Learning
Lecture 1 What You Will Learn in This Section 00:02:03 Duration
Lecture 2 Note on DP-100 Exam and New Studio 00:05:07 Duration
Lecture 3 The course slides as well as Data Files for all sections
Lecture 4 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 5 Why Machine Learning is the Future 00:09:43 Duration
Lecture 6 What is Machine Learning 00:09:31 Duration
Lecture 7 Understanding various aspects of data - Type, Variables, Category 00:07:06 Duration
Lecture 8 Common Machine Learning Terms - Probability, Mean, Mode, Median, Range 00:07:41 Duration
Lecture 9 Types of Machine Learning Models - Classification, Regression, Clustering etc 00:10:02 Duration

Section 2 : Getting Started with Azure ML
Lecture 1 What You Will Learn in This Section 00:02:08 Duration
Lecture 2 What is Azure ML and high level architecture 00:03:59 Duration
Lecture 3 Creating a Free Azure ML Account 00:03:24 Duration
Lecture 4 Azure ML Studio Overview and walk-through 00:05:01 Duration
Lecture 5 Azure ML Experiment Workflow 00:07:20 Duration
Lecture 6 Azure ML Cheat Sheet for Model Selection 00:06:02 Duration

Section 3 : Data Processing
Lecture 1 [Hands On] - Data Input-Output - Upload Data 00:08:18 Duration
Lecture 2 [Hands On] - Data Input-Output - Convert and Unpack 00:08:53 Duration
Lecture 3 [Hands On] - Data Input-Output - Import Data 00:05:46 Duration
Lecture 4 [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns 00:11:34 Duration
Lecture 5 [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata 00:18:30 Duration
Lecture 6 [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data 00:16:56 Duration
Lecture 7 Update to Lecture Sequence

Section 4 : Classification
Lecture 1 Logistic Regression - What is Logistic Regression 00:06:46 Duration
Lecture 2 [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model 00:22:09 Duration
Lecture 3 Logistic Regression - Understand Parameters and Their Impact 00:11:19 Duration
Lecture 4 Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score 00:13:17 Duration
Lecture 5 Logistic Regression - Model Selection and Impact Analysis 00:05:50 Duration
Lecture 6 [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model 00:08:13 Duration
Lecture 7 Decision Tree - What is Decision Tree 00:07:35 Duration
Lecture 8 Decision Tree - Ensemble Learning - Bagging and Boosting 00:07:06 Duration
Lecture 9 Decision Tree - Parameters - Two Class Boosted Decision Tree 00:05:35 Duration
Lecture 10 [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction 00:10:43 Duration
Lecture 11 Decision Forest - Parameters Explained 00:03:37 Duration
Lecture 12 [Hands On] - Two Class Decision Forest - Adult Census Income Prediction 00:14:43 Duration
Lecture 13 [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data 00:08:14 Duration
Lecture 14 SVM - What is Support Vector Machine
Lecture 15 [Hands On] - SVM - Adult Census Income Prediction 00:05:32 Duration

Section 5 : Hyperparameter Tuning
Lecture 1 [Hands On] - Tune Hyperparameter for Best Parameter Selection 00:09:53 Duration

Section 6 : Deploy Webservice
Lecture 1 Azure ML Webservice - Prepare the experiment for webservice 00:02:22 Duration
Lecture 2 [Hands On] - Deploy Machine Learning Model As a Web Service 00:03:28 Duration
Lecture 3 [Hands On] - Use the Web Service - Example of Excel 00:06:38 Duration

Section 7 : Regression Analysis
Lecture 1 What is Linear Regression 00:06:19 Duration
Lecture 2 Regression Analysis - Common Metrics 00:06:27 Duration
Lecture 3 [Hands On] - Linear Regression model using OLS 00:11:05 Duration
Lecture 4 [Hands On] - Linear Regression - R Squared 00:04:26 Duration
Lecture 5 Gradient Descent 00:10:49 Duration
Lecture 6 Linear Regression Online Gradient Descent 00:02:12 Duration
Lecture 7 [Hands On] - Experiment Online Gradient 00:04:21 Duration
Lecture 8 Decision Tree - What is Regression Tree 00:06:42 Duration
Lecture 9 Decision Tree - What is Boosted Decision Tree Regression 00:02:00 Duration
Lecture 10 [Hands On] - Decision Tree - Experiment Boosted Decision Tree 00:07:01 Duration

Section 8 : Clustering
Lecture 1 What is Cluster Analysis 00:11:52 Duration
Lecture 2 [Hands On] - Cluster Analysis Experiment 1 00:13:16 Duration
Lecture 3 [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate 00:08:04 Duration

Section 9 : Data Processing - Solving Data Processing Challenges
Lecture 1 Section Introduction 00:02:49 Duration
Lecture 2 How to Summarize Data 00:06:29 Duration
Lecture 3 [Hands On] - Summarize Data - Experiment 00:03:12 Duration
Lecture 4 Outliers Treatment - Clip Values 00:06:52 Duration
Lecture 5 [Hands On] - Outliers Treatment - Clip Values 00:07:51 Duration
Lecture 6 Clean Missing Data with MICE 00:07:19 Duration
Lecture 7 [Hands On] - Clean Missing Data with MICE 00:06:44 Duration
Lecture 8 SMOTE - Create New Synthetic Observations 00:08:34 Duration
Lecture 9 [Hands On] - SMOTE 00:05:50 Duration
Lecture 10 Data Normalization - Scale and Reduce 00:03:11 Duration
Lecture 11 [Hands On] - Data Normalization 00:02:32 Duration
Lecture 12 PCA - What is PCA and Curse of Dimensionality 00:06:24 Duration
Lecture 13 [Hands On] - Principal Component Analysis 00:03:24 Duration
Lecture 14 Join Data - Join Multiple Datasets based on common keys 00:06:03 Duration
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
Lecture 1 Feature Selection - Section Introduction
Lecture 2 Pearson Correlation Coefficient 00:07:12 Duration
Lecture 3 Chi Square Test of Independence 00:05:34 Duration
Lecture 4 Kendall Correlation Coefficient 00:04:11 Duration
Lecture 5 Spearman's Rank Correlation 00:03:43 Duration
Lecture 6 [Hands On] - Comparison Experiment for Correlation Coefficients 00:07:40 Duration
Lecture 7 [Hands On] - Filter Based Selection - AzureML Experiment 00:03:33 Duration
Lecture 8 Fisher Based LDA - Intuition 00:04:43 Duration
Lecture 9 [Hands On] - Fisher Based LDA - Experiment 00:05:46 Duration

Section 11 : Recommendation System
Lecture 1 What is a Recommendation System 00:16:57 Duration
Lecture 2 Data Preparation using Recommender Split 00:08:34 Duration
Lecture 3 What is Matchbox Recommender and Train Matchbox Recommender
Lecture 4 How to Score the Matchbox Recommender 00:05:43 Duration
Lecture 5 [Hands On] - Restaurant Recommendation Experiment 00:13:36 Duration
Lecture 6 Understanding the Matchbox Recommendation Results 00:08:58 Duration

Section 12 : Text Analytics and Natural Language Processing
Lecture 1 What is Text Analytics or Natural Language Processing 00:08:05 Duration
Lecture 2 Text Pre-Processing 00:14:06 Duration
Lecture 3 Bag Of Words and N-Gram Models for Text features 00:08:25 Duration
Lecture 4 Feature Hashing 00:14:48 Duration
Lecture 5 Note for the next Hands On
Lecture 6 [Hands On] - Classify Customer Complaints using Text Analytics 00:10:03 Duration

Section 13 : ------- DP - 100 Certification Exam ---------
Lecture 1 DP-100 Exam Curriculum 00:09:55 Duration

Section 14 : Azure Machine Learning with Studio Designer
Lecture 1 Understand the AzureMLService Architecture 00:07:57 Duration
Lecture 2 Create the AzureML Workspace 00:09:59 Duration
Lecture 3 View and Manage Workspace Settings 00:05:13 Duration
Lecture 4 Overview of New AzureML Studio 00:10:33 Duration
Lecture 5 DP-100 Exam Coverage So far 00:01:47 Duration
Lecture 6 What is AzureML Datastore and Dataset 00:06:50 Duration
Lecture 7 Create and Register a Datastore 00:11:53 Duration
Lecture 8 Create a Dataset 00:12:25 Duration
Lecture 9 Explore the AzureML Dataset 00:03:22 Duration
Lecture 10 Understanding the AzureML Compute Resources 00:08:08 Duration
Lecture 11 Create a Compute Cluster and Compute Instance 00:06:56 Duration
Lecture 12 What is an AzureML Pipeline 00:05:59 Duration
Lecture 13 Create a Pipeline using AzureML Designer 00:11:45 Duration
Lecture 14 Submit the Designer Pipeline run 00:11:56 Duration
Lecture 15 Create an Inference Pipeline 00:08:41 Duration
Lecture 16 Deploy a real-time endpoint using Designer 00:09:54 Duration
Lecture 17 Create a batch inference pipeline using 00:08:21 Duration
Lecture 18 Run a Batch Inference Pipeline from Designer 00:05:02 Duration

Section 15 : DesignerClassic Studio Vs Pandas and Scikit-learn
Lecture 1 A note on Anaconda and Spyder
Lecture 2 What this section is about 00:02:08 Duration
Lecture 3 Pandas - Import Data for Experiments 00:07:36 Duration
Lecture 4 Pandas - Import Data Part 2 00:05:16 Duration
Lecture 5 Select Columns using Pandas
Lecture 6 Select Columns By drop method 00:07:44 Duration
Lecture 7 Add columns and rows 00:07:01 Duration
Lecture 8 Clean Missing Data 00:07:07 Duration
Lecture 9 Edit Metadata of columns using Pandas 00:04:23 Duration
Lecture 10 Create Summary Statistics using describe 00:07:29 Duration
Lecture 11 Clip Values - Remove Outliers using Constants 00:05:52 Duration
Lecture 12 Clip Values - Remove Outliers with Percentiles 00:07:54 Duration
Lecture 13 Convert and Save a delimited file using Pandas 00:07:03 Duration
Lecture 14 Data Normalization 00:11:52 Duration
Lecture 15 Label Encoding of String Categorical data 00:09:48 Duration
Lecture 16 Why Hot encoding is required 00:03:31 Duration
Lecture 17 Hot Encoding using Pandas get_dummies 00:04:10 Duration
Lecture 18 Split The Data for training and testing 00:11:24 Duration
Lecture 19 Build Logistic Regression using Python - Part 1 00:04:23 Duration
Lecture 20 Build Logistic Regression using Python - Part 2 00:12:10 Duration

Section 16 : Azure Machine Learning with AzureML SDK
Lecture 1 Introduction to AzureML SDK 00:04:41 Duration
Lecture 2 Create AzureML Workspace using SDK 00:08:24 Duration
Lecture 3 Verify the Workspace and Write the Workspace Config File 00:03:32 Duration
Lecture 4 Create and Register a Datastore using AzureML SDK 00:10:57 Duration
Lecture 5 Create and Register a Dataset using SDK 00:11:23 Duration
Lecture 6 Access Workspace, Datastore and Datasets using SDK 00:11:28 Duration
Lecture 7 Pandas Dataframe and AzureML Dataset conversions 00:09:50 Duration
Lecture 8 Upload local data to storage account via datastore 00:10:20 Duration
Lecture 9 Run a sample experiment using AzureML SDK - Part 1 00:08:38 Duration
Lecture 10 Run a sample experiment using AzureML SDK - Part 2 00:10:56 Duration
Lecture 11 Problem Statement - Run a sample experiment and log values 00:02:50 Duration
Lecture 12 Run a script in Azureml environment - Part 1 00:04:33 Duration
Lecture 13 Run a script in Azureml environment - Part 2 00:07:28 Duration
Lecture 14 Run a script in Azureml environment - Part 3 00:08:29 Duration
Lecture 15 Run a script in Azureml environment - Part 4 00:07:54 Duration
Lecture 16 Run a script in Azureml environment - Part 5 00:06:27 Duration
Lecture 17 DP-100 Exam Coverage So far
Lecture 18 Train and Run a Model Script in AzureML Part 1 00:06:11 Duration
Lecture 19 Train and Run a Model Script in AzureML Part 2 00:10:32 Duration
Lecture 20 Train and Run a Model Script in AzureML Part 3 00:10:32 Duration
Lecture 21 Train and Run a Model Script in AzureML Part 4 00:05:01 Duration
Lecture 22 Train and Run a Model Script in AzureML Part 5 00:09:06 Duration
Lecture 23 Provisioning Compute Cluster using SDK 00:11:20 Duration
Lecture 24 Automate Model Training using AzureML SDK 00:07:52 Duration
Lecture 25 Automate Model Training - Define Pipeline Steps 00:14:22 Duration
Lecture 26 Automate Model Training - Define Run Configuration 00:08:04 Duration
Lecture 27 Automate Model Training - Define Build and Run 00:06:15 Duration
Lecture 28 Detour - Command Line Arguments 00:11:59 Duration
Lecture 29 Automate Model Training - Create Dataprep Step 00:14:01 Duration
Lecture 30 Automate Model Training - Create Training Step 00:03:40 Duration
Lecture 31 Run the pipeline and see the results 00:10:13 Duration

Section 17 : Azure AutoML
Lecture 1 To be Added

Section 18 : Azure Hyperdrive
Lecture 1 To be Added

Section 19 : Python Crash Course
Lecture 1 An Important Note
Lecture 2 Install Anaconda 00:05:26 Duration
Lecture 3 Hello World and Know your environment 00:05:38 Duration
Lecture 4 Variable Types in Python 00:09:20 Duration
Lecture 5 Conditional Statements in Python 00:06:03 Duration
Lecture 6 Python Loops explained 00:02:40 Duration
Lecture 7 While Loops in Python 00:05:36 Duration
Lecture 8 For Loop in Python 00:05:17 Duration
Lecture 9 Python Lists 00:01:58 Duration
Lecture 10 Python Lists - Operations Part 1 00:04:09 Duration
Lecture 11 Python Lists - Operations Part 2 00:02:33 Duration
Lecture 12 Multidimensional Lists in Python 00:04:32 Duration
Lecture 13 Slicing a multidimensional list 00:05:56 Duration
Lecture 14 Python Tuples 00:03:47 Duration
Lecture 15 Python Dictionary 00:03:41 Duration
Lecture 16 Python Dictionary Hands on Part 1 00:04:56 Duration
Lecture 17 Python Dictionary Hands on Part 2 00:04:22 Duration
Lecture 18 Python Functions 00:05:08 Duration
Lecture 19 Python Functions - Hands on 00:05:37 Duration
Lecture 20 Global Vs Local Variables in Python 00:08:39 Duration
Lecture 21 Types of Function Arguments 00:04:24 Duration
Lecture 22 Function Arguments - Required Arguments 00:07:49 Duration
Lecture 23 Function Arguments - Default Arguments 00:05:56 Duration
Lecture 24 Function Arguments - Keyword Arguments 00:07:50 Duration
Lecture 25 Object Oriented Programming 00:11:33 Duration
Lecture 26 Define a Class and Create an Object 00:14:54 Duration
Lecture 27 Initialize the Class Attributes using __init__ 00:08:56 Duration
Lecture 28 Packages and Modules in Python 00:05:59 Duration

Section 20 : Azure Fundamentals
Lecture 1 What is Cloud Computing 00:08:03 Duration
Lecture 2 What is Azure 00:04:11 Duration
Lecture 3 Azure Basic Terms and Concepts 00:05:09 Duration
Lecture 4 Azure Storage and Data Resource 00:09:34 Duration
Lecture 5 Azure Storage hands on 00:12:20 Duration
Lecture 6 Azure ComputeVirtual Machines 00:04:18 Duration
Lecture 7 Dockers and Azure Container Registry 00:05:47 Duration