BigQuery ML – Machine Learning in SQL using Google BigQuery

BigQuery ML – Machine Learning in SQL using Google BigQuery, available at $84.99, has an average rating of 4.7, with 125 lectures, 6 quizzes, based on 222 reviews, and has 2824 subscribers.

You will learn about 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. This course is ideal for individuals who are Machine Learning Engineers or Data analysts or Data scientists or Data Engineers It is particularly useful for Machine Learning Engineers or Data analysts or Data scientists or Data Engineers.

Enroll now: BigQuery ML – Machine Learning in SQL using Google BigQuery

Summary

Title: BigQuery ML – Machine Learning in SQL using Google BigQuery

Price: $84.99

Average Rating: 4.7

Number of Lectures: 125

Number of Quizzes: 6

Number of Published Lectures: 125

Number of Published Quizzes: 6

Number of Curriculum Items: 139

Number of Published Curriculum Objects: 139

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Machine Learning Engineers
Data analysts
Data scientists
Data Engineers

Target Audiences

Machine Learning Engineers
Data analysts
Data scientists
Data Engineers

“BigQuery ML lets you create and execute machine learning models in BigQuery usingstandard 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 regression
Logistic regression
K-means clustering
Boosted Tree
Deep neural networks
ARIMA+ Time series Forecasting
Product Component Analysis (PCA)
Matrix Factorization

After completing this course, you can confidently start creating production-grade Machine Learning models in Real-world corporate projects using BigQuery ML.

Add-Ons

Questions 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.

Course Curriculum

Chapter 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

Chapter 2: BigQuery ML (BQML) introduction

Lecture 1: What is BigQuery ML

Lecture 2: Conventional ML challenges and How Big query is addressing them

Lecture 3: BigQuery ML Features

Lecture 4: Advantages of BigQuery ML

Lecture 5: Lifecycle/Workflow of a BigQuery ML Project

Lecture 6: BQML supported models

Chapter 3: BigQuery Basics – Crash course

Lecture 1: Announcement

Lecture 2: Setup a GCP account

Lecture 3: Important Note

Lecture 4: Create a Project

Lecture 5: BigQuery UI Tour

Lecture 6: Create a Dataset

Lecture 7: Create a Table

Chapter 4: Linear Regression

Lecture 1: What is Linear regression – Part 1

Lecture 2: What is Linear regression – Part 2

Lecture 3: High-level view of Create Model query

Lecture 4: Limitations of Create model query

Lecture 5: Linear regression Example Use case

Lecture 6: Basic Options in Create model query

Lecture 7: Overfitting problem

Lecture 8: L2/Ridge regularization

Lecture 9: L1/Lasso regularization

Lecture 10: Gradient Descent Optimize Strategy

Lecture 11: Types of Gradient Descent

Lecture 12: Learn rate Option

Lecture 13: Other Options in Create model query

Lecture 14: Model Training – Write Create model Query for Linear regression

Lecture 15: Exploring Model details

Lecture 16: Model Evaluation Query (ML.EVALUATE)

Lecture 17: Model Training – Optimize Create Model Query

Lecture 18: ML.TRAINING_INFO Function

Lecture 19: Model Prediction (ML.PREDICT)

Chapter 5: Hyperparameter Tuning in BigQuery

Lecture 1: What is Hyperparameter Tuning ?

Lecture 2: Hyperparameter Tuning Options in BigQuery

Lecture 3: Tune the Linear regression model

Lecture 4: ML.TRIAL_INFO Function

Chapter 6: Model Explainability Functions

Lecture 1: Why Model Explainability is important ?

Lecture 2: Model Explainability Functions in BigQuery

Lecture 3: ML.WEIGHTS Function

Lecture 4: List of functions supported by all models

Chapter 7: Logistic regression

Lecture 1: What is Logistic regression ?

Lecture 2: Sigmoid Function

Lecture 3: Logistic regression Example Use case

Lecture 4: Model Training – Write Create model Query for Logistic regression

Lecture 5: Evaluation metrics Fundamentals explained

Lecture 6: Precision, Recall, Accuracy, F1 score

Lecture 7: Evaluation Functions in BigQuery

Lecture 8: Prediction Function (ML.PREDICT)

Lecture 9: Applications of Logistic regression

Chapter 8: Feature Pre-processing

Lecture 1: Automatic Feature Pre-processing

Lecture 2: Manual Feature Pre-processing – Part 1

Lecture 3: Manual Feature Pre-processing – Part 2

Lecture 4: FEATURE_INFO Function

Chapter 9: K-means Clustering

Lecture 1: What is Clustering

Lecture 2: K-means algorithm working

Lecture 3: Advantages & Disadvantages of K-means

Lecture 4: Applications of K-means algorithm

Lecture 5: Options in Create model query

Lecture 6: K-means Example in BigQuery – Create model

Lecture 7: K-means Example in BigQuery – Evaluation

Lecture 8: K-means Example in BigQuery – Prediction

Lecture 9: K-means Example in BigQuery – Anomaly detection

Chapter 10: Boosted Trees

Lecture 1: What is Boosting and Why it is needed

Lecture 2: Boosted Tree working explained

Lecture 3: Types of Boosting

Lecture 4: Options in Create model query – Part 1

Lecture 5: Options in Create model query – Part 2

Lecture 6: Boosted Tree Example – Use Case Intro & EDA

Lecture 7: Boosted Tree Example – Feature Engineering Part 1

Lecture 8: Boosted Tree Example – Feature Engineering Part 2

Lecture 9: Boosted Tree Example – Create model

Lecture 10: Boosted Tree Example – Hyperparameter Tuning

Lecture 11: Boosted Tree Example – Evaluation

Chapter 11: Model management Operations in BigQuery

Lecture 1: Introduction

Lecture 2: Operations on Models – Part 1

Lecture 3: Operations on Models – Part 2

Chapter 12: Deep Neural Network (DNN)

Instructors

J Garg – Real Time Learning
Data Engineering, Analytics and Cloud Trainer

Rating Distribution

1 stars: 4 votes
2 stars: 3 votes
3 stars: 28 votes
4 stars: 55 votes
5 stars: 132 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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