Bigquery Ml - Machine Learning In Sql Using Google Bigquery
Last updated 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.68 GB | Duration: 10h 48m
Last updated 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.68 GB | Duration: 10h 48m
Create Machine Learning models in Google Cloud Big Query using standard SQL. Big query ML course for ML & Data engineers
What you'll learn
BigQuery ML - Learn Machine Learning in Google Cloud using BigQuery.
Learn to Train, Evaluate, Inference, Tune and Explain Machine leaning models using standard SQL with Big Query.
Theory + BigQuery ML implementation of many Machine learning algorithms.
Detailed theory for each of the ML algorithm with a Real-world example implementation in BigQuery ML.
Linear regression, Logistic regression, K-means clustering, Boosted Tree.
Deep neural networks, ARIMA+ Time series Forecasting, Matrix Factorization, PCA.
Hyperparameter tuning of models, Model Explainability functions, Feature pre-processing functions, model management operations in BigQuery ML.
Requirements
Basic knowledge of SQL.
Description
"BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries."Big Query ML is a blessing for engineers who want to work in Machine Learning domain but lack programming language like Python, R. With Big Query ML, they can use their existing SQL knowledge to build operational production-grade Machine learning models.What's included in the course ?Brief introduction to various Machine Learning services of Google Cloud.Fundamentals of BigQuery ML and challenges which it solves. All of the Machine Learning algorithms are explained in 2 Steps :Step 1 : Theoretical explanation of working of an ML algorithm. Step 2 : Practical implementation of the ML algorithm in BigQuery ML.Each and every Machine learning algorithm is explained with HANDS-ON examples.Hyperparameter tuning of models, Model Explainability functions, Feature pre-processing functions.Model management operations using bq commands.BigQuery ML pricing (Flat rate & On-demand pricing models).Assignment for each Machine learning algorithm for self Hands-On in Big Query ML.Learn Best practices and Optimization techniques for BigQuery ML.Machine Learning algorithms explained:Linear regressionLogistic regressionK-means clustering Boosted TreeDeep neural networks ARIMA+ Time series Forecasting Product Component Analysis (PCA)Matrix FactorizationAfter completing this course, you can confidently start creating production-grade Machine Learning models in Real-world corporate projects using BigQuery ML.Add-OnsQuestions and Queries will be answered very quickly.Queries, datasets and references used in lectures are attached in the course for your convenience.I am going to update it frequently, every time adding new components of Bigquery ML.
Overview
Section 1: Introduction to GCP
Lecture 1 Introduction to Google Cloud Platform
Lecture 2 GCP vs AWS vs Azure - Why choose GCP
Lecture 3 AI & ML services in Google Cloud
Section 2: BigQuery ML (BQML) introduction
Lecture 4 What is BigQuery ML
Lecture 5 Conventional ML challenges and How Big query is addressing them
Lecture 6 BigQuery ML Features
Lecture 7 Advantages of BigQuery ML
Lecture 8 Lifecycle/Workflow of a BigQuery ML Project
Lecture 9 BQML supported models
Section 3: BigQuery Basics - Crash course
Lecture 10 Announcement
Lecture 11 Setup a GCP account
Lecture 12 Important Note
Lecture 13 Create a Project
Lecture 14 BigQuery UI Tour
Lecture 15 Create a Dataset
Lecture 16 Create a Table
Section 4: Linear Regression
Lecture 17 What is Linear regression - Part 1
Lecture 18 What is Linear regression - Part 2
Lecture 19 High-level view of Create Model query
Lecture 20 Limitations of Create model query
Lecture 21 Linear regression Example Use case
Lecture 22 Basic Options in Create model query
Lecture 23 Overfitting problem
Lecture 24 L2/Ridge regularization
Lecture 25 L1/Lasso regularization
Lecture 26 Gradient Descent Optimize Strategy
Lecture 27 Types of Gradient Descent
Lecture 28 Learn rate Option
Lecture 29 Other Options in Create model query
Lecture 30 Model Training - Write Create model Query for Linear regression
Lecture 31 Exploring Model details
Lecture 32 Model Evaluation Query (ML.EVALUATE)
Lecture 33 Model Training - Optimize Create Model Query
Lecture 34 ML.TRAINING_INFO Function
Lecture 35 Model Prediction (ML.PREDICT)
Section 5: Hyperparameter Tuning in BigQuery
Lecture 36 What is Hyperparameter Tuning ?
Lecture 37 Hyperparameter Tuning Options in BigQuery
Lecture 38 Tune the Linear regression model
Lecture 39 ML.TRIAL_INFO Function
Section 6: Model Explainability Functions
Lecture 40 Why Model Explainability is important ?
Lecture 41 Model Explainability Functions in BigQuery
Lecture 42 ML.WEIGHTS Function
Lecture 43 List of functions supported by all models
Section 7: Logistic regression
Lecture 44 What is Logistic regression ?
Lecture 45 Sigmoid Function
Lecture 46 Logistic regression Example Use case
Lecture 47 Model Training - Write Create model Query for Logistic regression
Lecture 48 Evaluation metrics Fundamentals explained
Lecture 49 Precision, Recall, Accuracy, F1 score
Lecture 50 Evaluation Functions in BigQuery
Lecture 51 Prediction Function (ML.PREDICT)
Lecture 52 Applications of Logistic regression
Section 8: Feature Pre-processing
Lecture 53 Automatic Feature Pre-processing
Lecture 54 Manual Feature Pre-processing - Part 1
Lecture 55 Manual Feature Pre-processing - Part 2
Lecture 56 FEATURE_INFO Function
Section 9: K-means Clustering
Lecture 57 What is Clustering
Lecture 58 K-means algorithm working
Lecture 59 Advantages & Disadvantages of K-means
Lecture 60 Applications of K-means algorithm
Lecture 61 Options in Create model query
Lecture 62 K-means Example in BigQuery - Create model
Lecture 63 K-means Example in BigQuery - Evaluation
Lecture 64 K-means Example in BigQuery - Prediction
Lecture 65 K-means Example in BigQuery - Anomaly detection
Section 10: Boosted Trees
Lecture 66 What is Boosting and Why it is needed
Lecture 67 Boosted Tree working explained
Lecture 68 Types of Boosting
Lecture 69 Options in Create model query - Part 1
Lecture 70 Options in Create model query - Part 2
Lecture 71 Boosted Tree Example - Use Case Intro & EDA
Lecture 72 Boosted Tree Example - Feature Engineering Part 1
Lecture 73 Boosted Tree Example - Feature Engineering Part 2
Lecture 74 Boosted Tree Example - Create model
Lecture 75 Boosted Tree Example - Hyperparameter Tuning
Lecture 76 Boosted Tree Example - Evaluation
Section 11: Model management Operations in BigQuery
Lecture 77 Introduction
Lecture 78 Operations on Models - Part 1
Lecture 79 Operations on Models - Part 2
Section 12: Deep Neural Network (DNN)
Lecture 80 What is Artificial Neural Network
Lecture 81 Working of Artificial Neural Network
Lecture 82 DNN working explained
Lecture 83 Activation Functions - Sigmoid, TanH
Lecture 84 Activation Functions - RELU
Lecture 85 Which Activation Function to choose?
Lecture 86 Dropout technique to avoid Overfitting
Lecture 87 HIDDEN_UNITS Option
Lecture 88 Optimizer in DNN
Lecture 89 Other Options in DNN
Lecture 90 DNN Example Use Case
Lecture 91 DNN Example Implementation in BigQuery ML
Section 13: Principal Component Analysis (PCA)
Lecture 92 What is dimensionality reduction ?
Lecture 93 Working logic of PCA
Lecture 94 Mathematics behind PCA (4 step procedure)
Lecture 95 BigQuery Model Options in PCA
Lecture 96 Creating PCA Model
Lecture 97 Evaluating and Inferencing PCA model
Section 14: BigQuery ML Pricing
Lecture 98 Free operations in BigQuery ML
Lecture 99 What is Flat rate pricing model
Lecture 100 Costs involved in Flat rate pricing model
Lecture 101 Reservations
Lecture 102 BigQuery ML On-demand pricing model
Lecture 103 Calculate price for Create Model query
Section 15: Matrix Factorization (Collaborative Filtering)
Lecture 104 Introduction to Matrix Factorization (Recommendation systems)
Lecture 105 Content Filtering
Lecture 106 Model based Collaborative Filtering
Lecture 107 Memory based Collaborative Filtering
Lecture 108 Create model Options
Lecture 109 Purchase BigQuery slots for Matrix Factorization
Lecture 110 Create Recommendation Model using Matrix Factorization
Lecture 111 Evaluation and Recommendation
Section 16: ARIMA+ for Time series Forecasting
Lecture 112 What is Time series Forecasting ?
Lecture 113 Components of Time series
Lecture 114 Stationarity in Time series
Lecture 115 Auto regression (AR) in ARIMA
Lecture 116 Moving Average (MA) in ARIMA
Lecture 117 ARIMA+ Options - Part 1
Lecture 118 ARIMA+ Options - Part 2
Lecture 119 ARIMA+ Example - Use case & EDA
Lecture 120 ARIMA+ Example - Create Model
Lecture 121 ARIMA+ Example - Evaluation
Lecture 122 ARIMA+ Example - Inferencing Functions
Lecture 123 ARIMA+ Example - Model Explainability
Section 17: Additional Learnings
Lecture 124 Google Cloud SDK setup
Section 18: BONUS
Lecture 125 Bonus
Machine Learning Engineers,Data analysts,Data scientists,Data Engineers