Section 1 : Basics of Machine Learning

Lecture 1 What You Will Learn in This Section copy 2:3
Lecture 2 The course slides as well as Data Files for all sections Text
Lecture 3 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 4 Why Machine Learning is the Future 9:36
Lecture 5 What is Machine Learning 9:31
Lecture 6 Understanding various aspects of data - Type, Variables, Category 7:6
Lecture 7 Common Machine Learning Terms - Probability, Mean, Mode, Median, Range 7:41
Lecture 8 Types of Machine Learning Models - Classification, Regression, Clustering etc

Section 2 : Getting Started with Azure ML

Lecture 9 What You Will Learn in This Section 2:8
Lecture 10 What is Azure ML and high level architecture 3:59
Lecture 11 Creating a Free Azure ML Account 3:24
Lecture 12 Azure ML Studio Overview and walk-through 5:1
Lecture 13 Azure ML Experiment Workflow 7:20
Lecture 14 Azure ML Cheat Sheet for Model Selection 6:2

Section 3 : Data Processing

Lecture 15 [Hands On] - Data Input-Output - Upload Data 8:18
Lecture 16 [Hands On] - Data Input-Output - Convert and Unpack 8:53
Lecture 17 [Hands On] - Data Input-Output - Import Data 5:46
Lecture 18 [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns
Lecture 19 [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata 18:30
Lecture 20 [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data 16:56
Lecture 21 Update to Lecture Sequence Text

Section 4 : Classification

Lecture 22 Logistic Regression - What is Logistic Regression
Lecture 23 [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model 22:9
Lecture 24 Logistic Regression - Understand Parameters and Their Impact 11:19
Lecture 25 Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score 13:17
Lecture 26 Logistic Regression - Model Selection and Impact Analysis 5:50
Lecture 27 [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model 8:13
Lecture 28 Decision Tree - What is Decision Tree 7:35
Lecture 29 Decision Tree - Ensemble Learning - Bagging and Boosting 7:6
Lecture 30 Decision Tree - Parameters - Two Class Boosted Decision Tree 5:35
Lecture 31 [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction 10:43
Lecture 32 Decision Forest - Parameters Explained 3:37
Lecture 33 [Hands On] - Two Class Decision Forest - Adult Census Income Prediction 14:43
Lecture 34 [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data 8:14
Lecture 35 SVM - What is Support Vector Machine 4:1
Lecture 36 [Hands On] - SVM - Adult Census Income Prediction 5:32

Section 5 : Hyperparameter Tuning

Lecture 37 [Hands On] - Tune Hyperparameter for Best Parameter Selection 9:53

Section 6 : Deploy Webservice

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

Section 7 : Regression Analysis

Lecture 41 What is Linear Regression 6:19
Lecture 42 Regression Analysis - Common Metrics 6:27
Lecture 43 [Hands On] - Linear Regression model using OLS 11:5
Lecture 44 [Hands On] - Linear Regression - R Squared 4:26
Lecture 45 Gradient Descent 10:49
Lecture 46 Linear Regression Online Gradient Descent 2:12
Lecture 47 [Hands On] - Experiment Online Gradient 4:21
Lecture 48 Decision Tree - What is Regression Tree 6:42
Lecture 49 Decision Tree - What is Boosted Decision Tree Regression 2:0
Lecture 50 [Hands On] - Decision Tree - Experiment Boosted Decision Tree 7:1

Section 8 : Clustering

Lecture 51 What is Cluster Analysis 11:52
Lecture 52 [Hands On] - Cluster Analysis Experiment 1 13:16
Lecture 53 [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate 8:4

Section 9 : Data Processing - Solving Data Processing Challenges

Lecture 54 Section Introduction 2:49
Lecture 55 How to Summarize Data 6:29
Lecture 56 [Hands On] - Summarize Data - Experiment 3:12
Lecture 57 Outliers Treatment - Clip Values 6:52
Lecture 58 [Hands On] - Outliers Treatment - Clip Values 7:51
Lecture 59 Clean Missing Data with MICE 7:19
Lecture 60 [Hands On] - Clean Missing Data with MICE 6:44
Lecture 61 SMOTE - Create New Synthetic Observations 8:34
Lecture 62 [Hands On] - SMOTE 5:50
Lecture 63 Data Normalization - Scale and Reduce 3:11
Lecture 64 [Hands On] - Data Normalization 2:32
Lecture 65 PCA - What is PCA and Curse of Dimensionality 6:24
Lecture 66 [Hands On] - Principal Component Analysis 3:24
Lecture 67 Join Data - Join Multiple Datasets based on common keys 6:3
Lecture 68 [Hands On] - Join Data - Experiment 2:43

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

Lecture 69 Feature Selection - Section Introduction 5:49
Lecture 70 Pearson Correlation Coefficient 7:12
Lecture 71 Chi Square Test of Independence 5:34
Lecture 72 Kendall Correlation Coefficient 4:11
Lecture 73 Spearman's Rank Correlation 3:43
Lecture 74 [Hands On] - Comparison Experiment for Correlation Coefficients 7:40
Lecture 75 [Hands On] - Filter Based Selection - AzureML Experiment 3:33
Lecture 76 Fisher Based LDA - Intuition 4:43
Lecture 77 [Hands On] - Fisher Based LDA - Experiment

Section 11 : Recommendation System

Lecture 78 What is a Recommendation System
Lecture 79 Data Preparation using Recommender Split 8:34
Lecture 80 What is Matchbox Recommender and Train Matchbox Recommender 8:34
Lecture 81 How to Score the Matchbox Recommender 5:43
Lecture 82 [Hands On] - Restaurant Recommendation Experiment 13:36
Lecture 83 Understanding the Matchbox Recommendation Results 8:58

Section 12 : Text Analytics and Natural Language Processing

Lecture 84 What is Text Analytics or Natural Language Processing 8:5
Lecture 85 Text Pre-Processing 14:6
Lecture 86 Bag Of Words and N-Gram Models for Text features 8:25
Lecture 87 Feature Hashing 14:48
Lecture 88 Note for the next Hands On Text
Lecture 89 [Hands On] - Classify Customer Complaints using Text Analytics 10:3