Section 1 : Basics of Machine Learning

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

Section 2 : Getting Started with Azure ML

Lecture 10 What You Will Learn in This Section 2:8
Lecture 11 What is Azure ML and high level architecture 3:59
Lecture 12 Creating a Free Azure ML Account 3:24
Lecture 13 Azure ML Studio Overview and walk-through 5:1
Lecture 14 Azure ML Experiment Workflow 7:20
Lecture 15 Azure ML Cheat Sheet for Model Selection 6:2

Section 3 : Data Processing

Lecture 16 [Hands On] - Data Input-Output - Upload Data 8:18
Lecture 17 [Hands On] - Data Input-Output - Convert and Unpack 8:53
Lecture 18 [Hands On] - Data Input-Output - Import Data 5:46
Lecture 19 [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns 11:34
Lecture 20 [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata 18:30
Lecture 21 [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data 16:56
Lecture 22 Update to Lecture Sequence Text

Section 4 : Classification

Lecture 23 Logistic Regression - What is Logistic Regression 6:46
Lecture 24 [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model 22:9
Lecture 25 Logistic Regression - Understand Parameters and Their Impact 11:19
Lecture 26 Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score 13:17
Lecture 27 Logistic Regression - Model Selection and Impact Analysis 5:50
Lecture 28 [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model 8:13
Lecture 29 Decision Tree - What is Decision Tree 7:35
Lecture 30 Decision Tree - Ensemble Learning - Bagging and Boosting 7:6
Lecture 31 Decision Tree - Parameters - Two Class Boosted Decision Tree 5:35
Lecture 32 [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction 10:43
Lecture 33 Decision Forest - Parameters Explained 3:37
Lecture 34 [Hands On] - Two Class Decision Forest - Adult Census Income Prediction 14:43
Lecture 35 [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data 8:14
Lecture 36 SVM - What is Support Vector Machine
Lecture 37 [Hands On] - SVM - Adult Census Income Prediction 5:32

Section 5 : Hyperparameter Tuning

Lecture 38 [Hands On] - Tune Hyperparameter for Best Parameter Selection 9:53

Section 6 : Deploy Webservice

Lecture 39 Azure ML Webservice - Prepare the experiment for webservice 2:22
Lecture 40 [Hands On] - Deploy Machine Learning Model As a Web Service 3:28
Lecture 41 [Hands On] - Use the Web Service - Example of Excel 6:38

Section 7 : Regression Analysis

Lecture 42 What is Linear Regression 6:19
Lecture 43 Regression Analysis - Common Metrics 6:27
Lecture 44 [Hands On] - Linear Regression model using OLS 11:5
Lecture 45 [Hands On] - Linear Regression - R Squared 4:26
Lecture 46 Gradient Descent 10:49
Lecture 47 Linear Regression Online Gradient Descent 2:12
Lecture 48 [Hands On] - Experiment Online Gradient 4:21
Lecture 49 Decision Tree - What is Regression Tree 6:42
Lecture 50 Decision Tree - What is Boosted Decision Tree Regression 2:0
Lecture 51 [Hands On] - Decision Tree - Experiment Boosted Decision Tree 7:1

Section 8 : Clustering

Lecture 52 What is Cluster Analysis 11:52
Lecture 53 [Hands On] - Cluster Analysis Experiment 1 13:16
Lecture 54 [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate 8:4

Section 9 : Data Processing - Solving Data Processing Challenges

Lecture 55 Section Introduction 2:49
Lecture 56 How to Summarize Data 6:29
Lecture 57 [Hands On] - Summarize Data - Experiment 3:12
Lecture 58 Outliers Treatment - Clip Values 6:52
Lecture 59 [Hands On] - Outliers Treatment - Clip Values 7:51
Lecture 60 Clean Missing Data with MICE 7:19
Lecture 61 [Hands On] - Clean Missing Data with MICE 6:44
Lecture 62 SMOTE - Create New Synthetic Observations 8:34
Lecture 63 [Hands On] - SMOTE 5:50
Lecture 64 Data Normalization - Scale and Reduce 3:11
Lecture 65 [Hands On] - Data Normalization 2:32
Lecture 66 PCA - What is PCA and Curse of Dimensionality 6:24
Lecture 67 [Hands On] - Principal Component Analysis 3:24
Lecture 68 Join Data - Join Multiple Datasets based on common keys 6:3
Lecture 69 [Hands On] - Join Data - Experiment 2:43

Section 10 : Feature Selection - Select a subset of Variables or features with highest impact

Lecture 70 Feature Selection - Section Introduction
Lecture 71 Pearson Correlation Coefficient 7:12
Lecture 72 Chi Square Test of Independence 5:34
Lecture 73 Kendall Correlation Coefficient 4:11
Lecture 74 Spearman's Rank Correlation 3:43
Lecture 75 [Hands On] - Comparison Experiment for Correlation Coefficients 7:40
Lecture 76 [Hands On] - Filter Based Selection - AzureML Experiment 3:33
Lecture 77 Fisher Based LDA - Intuition 4:43
Lecture 78 [Hands On] - Fisher Based LDA - Experiment 5:46

Section 11 : Recommendation System

Lecture 79 What is a Recommendation System 16:57
Lecture 80 Data Preparation using Recommender Split 8:34
Lecture 81 What is Matchbox Recommender and Train Matchbox Recommender
Lecture 82 How to Score the Matchbox Recommender 5:43
Lecture 83 [Hands On] - Restaurant Recommendation Experiment 13:36
Lecture 84 Understanding the Matchbox Recommendation Results 8:58

Section 12 : Text Analytics and Natural Language Processing

Lecture 85 What is Text Analytics or Natural Language Processing 8:5
Lecture 86 Text Pre-Processing 14:6
Lecture 87 Bag Of Words and N-Gram Models for Text features 8:25
Lecture 88 Feature Hashing 14:48
Lecture 89 Note for the next Hands On Text
Lecture 90 [Hands On] - Classify Customer Complaints using Text Analytics 10:3

Section 13 : ------- DP - 100 Certification Exam ---------

Lecture 91 DP-100 Exam Curriculum 9:55

Section 14 : Azure Machine Learning with Studio Designer

Lecture 92 Understand the AzureMLService Architecture 7:57
Lecture 93 Create the AzureML Workspace 9:59
Lecture 94 View and Manage Workspace Settings 5:13
Lecture 95 Overview of New AzureML Studio 10:33
Lecture 96 DP-100 Exam Coverage So far 1:47
Lecture 97 What is AzureML Datastore and Dataset 6:50
Lecture 98 Create and Register a Datastore 11:53
Lecture 99 Create a Dataset 12:25
Lecture 100 Explore the AzureML Dataset 3:22
Lecture 101 Understanding the AzureML Compute Resources 8:8
Lecture 102 Create a Compute Cluster and Compute Instance 6:56
Lecture 103 What is an AzureML Pipeline 5:59
Lecture 104 Create a Pipeline using AzureML Designer 11:45
Lecture 105 Submit the Designer Pipeline run 11:56
Lecture 106 Create an Inference Pipeline 8:41
Lecture 107 Deploy a real-time endpoint using Designer 9:54
Lecture 108 Create a batch inference pipeline using 8:21
Lecture 109 Run a Batch Inference Pipeline from Designer 5:2

Section 15 : DesignerClassic Studio Vs Pandas and Scikit-learn

Lecture 110 A note on Anaconda and Spyder Text
Lecture 111 What this section is about 2:8
Lecture 112 Pandas - Import Data for Experiments 7:36
Lecture 113 Pandas - Import Data Part 2 5:16
Lecture 114 Select Columns using Pandas
Lecture 115 Select Columns By drop method 7:44
Lecture 116 Add columns and rows 7:1
Lecture 117 Clean Missing Data 7:7
Lecture 118 Edit Metadata of columns using Pandas 4:23
Lecture 119 Create Summary Statistics using describe 7:29
Lecture 120 Clip Values - Remove Outliers using Constants 5:52
Lecture 121 Clip Values - Remove Outliers with Percentiles 7:54
Lecture 122 Convert and Save a delimited file using Pandas 7:3
Lecture 123 Data Normalization 11:52
Lecture 124 Label Encoding of String Categorical data 9:48
Lecture 125 Why Hot encoding is required 3:31
Lecture 126 Hot Encoding using Pandas get_dummies 4:10
Lecture 127 Split The Data for training and testing 11:24
Lecture 128 Build Logistic Regression using Python - Part 1 4:23
Lecture 129 Build Logistic Regression using Python - Part 2 12:10

Section 16 : Azure Machine Learning with AzureML SDK

Lecture 130 Introduction to AzureML SDK 4:41
Lecture 131 Create AzureML Workspace using SDK 8:24
Lecture 132 Verify the Workspace and Write the Workspace Config File 3:32
Lecture 133 Create and Register a Datastore using AzureML SDK 10:57
Lecture 134 Create and Register a Dataset using SDK 11:23
Lecture 135 Access Workspace, Datastore and Datasets using SDK 11:28
Lecture 136 Pandas Dataframe and AzureML Dataset conversions 9:50
Lecture 137 Upload local data to storage account via datastore 10:20
Lecture 138 Run a sample experiment using AzureML SDK - Part 1 8:38
Lecture 139 Run a sample experiment using AzureML SDK - Part 2 10:56
Lecture 140 Problem Statement - Run a sample experiment and log values 2:50
Lecture 141 Run a script in Azureml environment - Part 1 4:33
Lecture 142 Run a script in Azureml environment - Part 2 7:28
Lecture 143 Run a script in Azureml environment - Part 3 8:29
Lecture 144 Run a script in Azureml environment - Part 4 7:54
Lecture 145 Run a script in Azureml environment - Part 5 6:27
Lecture 146 DP-100 Exam Coverage So far
Lecture 147 Train and Run a Model Script in AzureML Part 1 6:11
Lecture 148 Train and Run a Model Script in AzureML Part 2 10:32
Lecture 149 Train and Run a Model Script in AzureML Part 3 10:32
Lecture 150 Train and Run a Model Script in AzureML Part 4 5:1
Lecture 151 Train and Run a Model Script in AzureML Part 5 9:6
Lecture 152 Provisioning Compute Cluster using SDK 11:20
Lecture 153 Automate Model Training using AzureML SDK 7:52
Lecture 154 Automate Model Training - Define Pipeline Steps 14:22
Lecture 155 Automate Model Training - Define Run Configuration 8:4
Lecture 156 Automate Model Training - Define Build and Run 6:15
Lecture 157 Detour - Command Line Arguments 11:59
Lecture 158 Automate Model Training - Create Dataprep Step 14:1
Lecture 159 Automate Model Training - Create Training Step 3:40
Lecture 160 Run the pipeline and see the results 10:13

Section 17 : Azure AutoML

Lecture 161 To be Added Text

Section 18 : Azure Hyperdrive

Lecture 162 To be Added Text

Section 19 : Python Crash Course

Lecture 163 An Important Note Text
Lecture 164 Install Anaconda 5:26
Lecture 165 Hello World and Know your environment 5:38
Lecture 166 Variable Types in Python 9:20
Lecture 167 Conditional Statements in Python 6:3
Lecture 168 Python Loops explained 2:40
Lecture 169 While Loops in Python 5:36
Lecture 170 For Loop in Python 5:17
Lecture 171 Python Lists 1:58
Lecture 172 Python Lists - Operations Part 1 4:9
Lecture 173 Python Lists - Operations Part 2 2:33
Lecture 174 Multidimensional Lists in Python 4:32
Lecture 175 Slicing a multidimensional list 5:56
Lecture 176 Python Tuples 3:47
Lecture 177 Python Dictionary 3:41
Lecture 178 Python Dictionary Hands on Part 1 4:56
Lecture 179 Python Dictionary Hands on Part 2 4:22
Lecture 180 Python Functions 5:8
Lecture 181 Python Functions - Hands on 5:37
Lecture 182 Global Vs Local Variables in Python 8:39
Lecture 183 Types of Function Arguments 4:24
Lecture 184 Function Arguments - Required Arguments 7:49
Lecture 185 Function Arguments - Default Arguments 5:56
Lecture 186 Function Arguments - Keyword Arguments 7:50
Lecture 187 Object Oriented Programming 11:33
Lecture 188 Define a Class and Create an Object 14:54
Lecture 189 Initialize the Class Attributes using __init__ 8:56
Lecture 190 Packages and Modules in Python 5:59

Section 20 : Azure Fundamentals

Lecture 191 What is Cloud Computing 8:3
Lecture 192 What is Azure 4:11
Lecture 193 Azure Basic Terms and Concepts 5:9
Lecture 194 Azure Storage and Data Resource 9:34
Lecture 195 Azure Storage hands on 12:20
Lecture 196 Azure ComputeVirtual Machines 4:18
Lecture 197 Dockers and Azure Container Registry 5:47