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    Bigquery Ml - Machine Learning In Sql Using Google Bigquery

    Posted By: ELK1nG
    Bigquery Ml - Machine Learning In Sql Using Google Bigquery

    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

    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