Section 1 : Introduction to GCP

Lecture 1 Introduction to Google Cloud Platform 2:14
Lecture 2 GCP vs AWS vs Azure - Why choose GCP 6:4
Lecture 3 AI & ML services in Google Cloud 8:55

Section 2 : BigQuery ML (BQML) introduction

Lecture 4 What is BigQuery ML 4:38
Lecture 5 Conventional ML challenges and How Big query is addressing them 7:37
Lecture 6 BigQuery ML Features 6:36
Lecture 7 Advantages of BigQuery ML 4:2
Lecture 8 LifecycleWorkflow of a BigQuery ML Project 4:56
Lecture 9 BQML supported models 5:47

Section 3 : BigQuery Basics - Crash course

Lecture 10 Announcement 1:26
Lecture 11 Setup a GCP account Text
Lecture 12 Important Note Text
Lecture 13 Create a Project 4:25
Lecture 14 BigQuery UI Tour 4:2
Lecture 15 Create a Dataset 4:29
Lecture 16 Create a Table 8:1

Section 4 : Linear Regression

Lecture 17 What is Linear regression - Part 1 5:53
Lecture 18 What is Linear regression - Part 2 4:0
Lecture 19 High-level view of Create Model query 7:3
Lecture 20 Limitations of Create model query 3:2
Lecture 21 Linear regression Example Use case 5:3
Lecture 22 Basic Options in Create model query 6:27
Lecture 23 Overfitting problem 3:54
Lecture 24 L2Ridge regularization 5:42
Lecture 25 L1Lasso regularization 3:12
Lecture 26 Gradient Descent Optimize Strategy 6:45
Lecture 27 Types of Gradient Descent 2:52
Lecture 28 Learn rate Option 4:11
Lecture 29 Other Options in Create model query 7:49
Lecture 30 Model Training - Write Create model Query for Linear regression 9:13
Lecture 31 Exploring Model details 3:31
Lecture 32 Model Evaluation Query (ML 7:44
Lecture 33 Model Training - Optimize Create Model Query 6:56
Lecture 34 ML Text
Lecture 35 Model Prediction (ML 6:12

Section 5 : Hyperparameter Tuning in BigQuery

Lecture 36 What is Hyperparameter Tuning 2:39
Lecture 37 Hyperparameter Tuning Options in BigQuery 8:31
Lecture 38 Tune the Linear regression model 6:1
Lecture 39 ML Text

Section 6 : Model Explainability Functions

Lecture 40 Why Model Explainability is important 3:40
Lecture 41 Model Explainability Functions in BigQuery
Lecture 42 ML
Lecture 43 List of functions supported by all models Text

Section 7 : Logistic regression

Lecture 44 What is Logistic regression 3:9
Lecture 45 Sigmoid Function 3:42
Lecture 46 Logistic regression Example Use case 4:44
Lecture 47 Model Training - Write Create model Query for Logistic regression 3:4
Lecture 48 Evaluation metrics Fundamentals explained 6:55
Lecture 49 Precision, Recall, Accuracy, F1 score 7:38
Lecture 50 Evaluation Functions in BigQuery 5:15
Lecture 51 Prediction Function (ML 4:12
Lecture 52 Applications of Logistic regression 2:16

Section 8 : Feature Pre-processing

Lecture 53 Automatic Feature Pre-processing 9:32
Lecture 54 Manual Feature Pre-processing - Part 1 10:25
Lecture 55 Manual Feature Pre-processing - Part 2 3:7
Lecture 56 FEATURE_INFO Function Text

Section 9 : K-means Clustering

Lecture 57 What is Clustering 4:0
Lecture 58 K-means algorithm working 4:10
Lecture 59 Advantages & Disadvantages of K-means 3:27
Lecture 60 Applications of K-means algorithm 3:1
Lecture 61 Options in Create model query 6:43
Lecture 62 K-means Example in BigQuery - Create model 7:54
Lecture 63 K-means Example in BigQuery - Evaluation 6:3
Lecture 64 K-means Example in BigQuery - Prediction 4:9
Lecture 65 K-means Example in BigQuery - Anomaly detection 5:43

Section 10 : Boosted Trees

Lecture 66 What is Boosting and Why it is needed
Lecture 67 Boosted Tree working explained 4:40
Lecture 68 Types of Boosting 5:22
Lecture 69 Options in Create model query - Part 1 8:15
Lecture 70 Options in Create model query - Part 2 8:50
Lecture 71 Boosted Tree Example - Use Case Intro & EDA 5:55
Lecture 72 Boosted Tree Example - Feature Engineering Part 1 2:33
Lecture 73 Boosted Tree Example - Feature Engineering Part 2 10:49
Lecture 74 Boosted Tree Example - Create model
Lecture 75 Boosted Tree Example - Hyperparameter Tuning 5:28
Lecture 76 Boosted Tree Example - Evaluation 4:39

Section 11 : Model management Operations in BigQuery

Lecture 77 Introduction 2:31
Lecture 78 Operations on Models - Part 1 5:31
Lecture 79 Operations on Models - Part 2 7:52

Section 12 : Deep Neural Network (DNN)

Lecture 80 What is Artificial Neural Network 5:41
Lecture 81 Working of Artificial Neural Network 8:10
Lecture 82 DNN working explained 7:15
Lecture 83 Activation Functions - Sigmoid, TanH 8:1
Lecture 84 Activation Functions - RELU 5:31
Lecture 85 Which Activation Function to choose 3:11
Lecture 86 Dropout technique to avoid Overfitting 5:8
Lecture 87 HIDDEN_UNITS Option 5:8
Lecture 88 Optimizer in DNN 4:25
Lecture 89 Other Options in DNN Text
Lecture 90 DNN Example Use Case 5:34
Lecture 91 DNN Example Implementation in BigQuery ML 7:12

Section 13 : BigQuery ML Pricing

Lecture 92 Free operations in BigQuery ML 2:15
Lecture 93 What is Flat rate pricing model 4:45
Lecture 94 Costs involved in Flat rate pricing model 6:0
Lecture 95 Reservations 5:50
Lecture 96 BigQuery ML On-demand pricing model 4:46
Lecture 97 Calculate price for Create Model query 3:39

Section 14 : ARIMA+ for Time series Forecasting

Lecture 98 What is Time series Forecasting
Lecture 99 Components of Time series 3:7
Lecture 100 Stationarity in Time series 3:22
Lecture 101 Auto regression (AR) in ARIMA 7:34
Lecture 102 Moving Average (MA) in ARIMA 7:14
Lecture 103 ARIMA+ Options - Part 1 6:35
Lecture 104 ARIMA+ Options - Part 2 3:40
Lecture 105 ARIMA+ Example - Use case & EDA 6:17
Lecture 106 ARIMA+ Example - Create Model 7:57
Lecture 107 ARIMA+ Example - Evaluation 10:12
Lecture 108 ARIMA+ Example - Inferencing Functions 6:25
Lecture 109 ARIMA+ Example - Model Explainability 5:26

Section 15 : Additional Learnings

Lecture 110 Google Cloud SDK setup 1:47