Section 1 : Introduction to GCP
Lecture 1 Introduction to Google Cloud Platform 00:02:14 Duration
Lecture 2 GCP vs AWS vs Azure - Why choose GCP 00:06:04 Duration
Lecture 3 AI & ML services in Google Cloud 00:08:55 Duration

Section 2 : BigQuery ML (BQML) introduction
Lecture 1 What is BigQuery ML 00:04:38 Duration
Lecture 2 Conventional ML challenges and How Big query is addressing them 00:07:37 Duration
Lecture 3 BigQuery ML Features 00:06:36 Duration
Lecture 4 Advantages of BigQuery ML 00:04:02 Duration
Lecture 5 LifecycleWorkflow of a BigQuery ML Project 00:04:56 Duration
Lecture 6 BQML supported models 00:05:47 Duration

Section 3 : BigQuery Basics - Crash course
Lecture 1 Announcement 00:01:26 Duration
Lecture 2 Setup a GCP account
Lecture 3 Important Note
Lecture 4 Create a Project 00:04:25 Duration
Lecture 5 BigQuery UI Tour 00:04:02 Duration
Lecture 6 Create a Dataset 00:04:29 Duration
Lecture 7 Create a Table 00:08:01 Duration

Section 4 : Linear Regression
Lecture 1 What is Linear regression - Part 1 00:05:53 Duration
Lecture 2 What is Linear regression - Part 2 00:04:00 Duration
Lecture 3 High-level view of Create Model query 00:07:03 Duration
Lecture 4 Limitations of Create model query 00:03:02 Duration
Lecture 5 Linear regression Example Use case 00:05:03 Duration
Lecture 6 Basic Options in Create model query 00:06:27 Duration
Lecture 7 Overfitting problem 00:03:54 Duration
Lecture 8 L2Ridge regularization 00:05:42 Duration
Lecture 9 L1Lasso regularization 00:03:12 Duration
Lecture 10 Gradient Descent Optimize Strategy 00:06:45 Duration
Lecture 11 Types of Gradient Descent 00:02:52 Duration
Lecture 12 Learn rate Option 00:04:11 Duration
Lecture 13 Other Options in Create model query 00:07:49 Duration
Lecture 14 Model Training - Write Create model Query for Linear regression 00:09:13 Duration
Lecture 15 Exploring Model details 00:03:31 Duration
Lecture 16 Model Evaluation Query (ML 00:07:44 Duration
Lecture 17 Model Training - Optimize Create Model Query 00:06:56 Duration
Lecture 18 ML
Lecture 19 Model Prediction (ML 00:06:12 Duration

Section 5 : Hyperparameter Tuning in BigQuery
Lecture 1 What is Hyperparameter Tuning 00:02:39 Duration
Lecture 2 Hyperparameter Tuning Options in BigQuery 00:08:31 Duration
Lecture 3 Tune the Linear regression model 00:06:01 Duration
Lecture 4 ML

Section 6 : Model Explainability Functions
Lecture 1 Why Model Explainability is important 00:03:40 Duration
Lecture 2 Model Explainability Functions in BigQuery
Lecture 3 ML
Lecture 4 List of functions supported by all models

Section 7 : Logistic regression
Lecture 1 What is Logistic regression 00:03:09 Duration
Lecture 2 Sigmoid Function 00:03:42 Duration
Lecture 3 Logistic regression Example Use case 00:04:44 Duration
Lecture 4 Model Training - Write Create model Query for Logistic regression 00:03:04 Duration
Lecture 5 Evaluation metrics Fundamentals explained 00:06:55 Duration
Lecture 6 Precision, Recall, Accuracy, F1 score 00:07:38 Duration
Lecture 7 Evaluation Functions in BigQuery 00:05:15 Duration
Lecture 8 Prediction Function (ML 00:04:12 Duration
Lecture 9 Applications of Logistic regression 00:02:16 Duration

Section 8 : Feature Pre-processing
Lecture 1 Automatic Feature Pre-processing 00:09:32 Duration
Lecture 2 Manual Feature Pre-processing - Part 1 00:10:25 Duration
Lecture 3 Manual Feature Pre-processing - Part 2 00:03:07 Duration
Lecture 4 FEATURE_INFO Function

Section 9 : K-means Clustering
Lecture 1 What is Clustering 00:04:00 Duration
Lecture 2 K-means algorithm working 00:04:10 Duration
Lecture 3 Advantages & Disadvantages of K-means 00:03:27 Duration
Lecture 4 Applications of K-means algorithm 00:03:01 Duration
Lecture 5 Options in Create model query 00:06:43 Duration
Lecture 6 K-means Example in BigQuery - Create model 00:07:54 Duration
Lecture 7 K-means Example in BigQuery - Evaluation 00:06:03 Duration
Lecture 8 K-means Example in BigQuery - Prediction 00:04:09 Duration
Lecture 9 K-means Example in BigQuery - Anomaly detection 00:05:43 Duration

Section 10 : Boosted Trees
Lecture 1 What is Boosting and Why it is needed
Lecture 2 Boosted Tree working explained 00:04:40 Duration
Lecture 3 Types of Boosting 00:05:22 Duration
Lecture 4 Options in Create model query - Part 1 00:08:15 Duration
Lecture 5 Options in Create model query - Part 2 00:08:50 Duration
Lecture 6 Boosted Tree Example - Use Case Intro & EDA 00:05:55 Duration
Lecture 7 Boosted Tree Example - Feature Engineering Part 1 00:02:33 Duration
Lecture 8 Boosted Tree Example - Feature Engineering Part 2 00:10:49 Duration
Lecture 9 Boosted Tree Example - Create model
Lecture 10 Boosted Tree Example - Hyperparameter Tuning 00:05:28 Duration
Lecture 11 Boosted Tree Example - Evaluation 00:04:39 Duration

Section 11 : Model management Operations in BigQuery
Lecture 1 Introduction 00:02:31 Duration
Lecture 2 Operations on Models - Part 1 00:05:31 Duration
Lecture 3 Operations on Models - Part 2 00:07:52 Duration

Section 12 : Deep Neural Network (DNN)
Lecture 1 What is Artificial Neural Network 00:05:41 Duration
Lecture 2 Working of Artificial Neural Network 00:08:10 Duration
Lecture 3 DNN working explained 00:07:15 Duration
Lecture 4 Activation Functions - Sigmoid, TanH 00:08:01 Duration
Lecture 5 Activation Functions - RELU 00:05:31 Duration
Lecture 6 Which Activation Function to choose 00:03:11 Duration
Lecture 7 Dropout technique to avoid Overfitting 00:05:08 Duration
Lecture 8 HIDDEN_UNITS Option 00:05:08 Duration
Lecture 9 Optimizer in DNN 00:04:25 Duration
Lecture 10 Other Options in DNN
Lecture 11 DNN Example Use Case 00:05:34 Duration
Lecture 12 DNN Example Implementation in BigQuery ML 00:07:12 Duration

Section 13 : BigQuery ML Pricing
Lecture 1 Free operations in BigQuery ML 00:02:15 Duration
Lecture 2 What is Flat rate pricing model 00:04:45 Duration
Lecture 3 Costs involved in Flat rate pricing model 00:06:00 Duration
Lecture 4 Reservations 00:05:50 Duration
Lecture 5 BigQuery ML On-demand pricing model 00:04:46 Duration
Lecture 6 Calculate price for Create Model query 00:03:39 Duration

Section 14 : ARIMA+ for Time series Forecasting
Lecture 1 What is Time series Forecasting
Lecture 2 Components of Time series 00:03:07 Duration
Lecture 3 Stationarity in Time series 00:03:22 Duration
Lecture 4 Auto regression (AR) in ARIMA 00:07:34 Duration
Lecture 5 Moving Average (MA) in ARIMA 00:07:14 Duration
Lecture 6 ARIMA+ Options - Part 1 00:06:35 Duration
Lecture 7 ARIMA+ Options - Part 2 00:03:40 Duration
Lecture 8 ARIMA+ Example - Use case & EDA 00:06:17 Duration
Lecture 9 ARIMA+ Example - Create Model 00:07:57 Duration
Lecture 10 ARIMA+ Example - Evaluation 00:10:12 Duration
Lecture 11 ARIMA+ Example - Inferencing Functions 00:06:25 Duration
Lecture 12 ARIMA+ Example - Model Explainability 00:05:26 Duration

Section 15 : Additional Learnings
Lecture 1 Google Cloud SDK setup 00:01:47 Duration