Google BigQuery ML Machine Learning in SQL (without Python)

Google BigQuery ML Machine Learning in SQL (without Python), available at $74.99, has an average rating of 4.4, with 48 lectures, 36 quizzes, based on 19 reviews, and has 241 subscribers.

You will learn about Create Machine Learning model and make prediction using only SQL code Evaluate and interpret model prediction quality Do Feature Engineering on different data types Clean up and limit data source with understanding of consequence of it This course is ideal for individuals who are Beginner Data Analysts or students who want to start with Machine Learning using just SQL It is particularly useful for Beginner Data Analysts or students who want to start with Machine Learning using just SQL.

Enroll now: Google BigQuery ML Machine Learning in SQL (without Python)

Summary

Title: Google BigQuery ML Machine Learning in SQL (without Python)

Price: $74.99

Average Rating: 4.4

Number of Lectures: 48

Number of Quizzes: 36

Number of Published Lectures: 48

Number of Published Quizzes: 36

Number of Curriculum Items: 110

Number of Published Curriculum Objects: 110

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Create Machine Learning model and make prediction using only SQL code
Evaluate and interpret model prediction quality
Do Feature Engineering on different data types
Clean up and limit data source with understanding of consequence of it

Who Should Attend

Beginner Data Analysts or students who want to start with Machine Learning using just SQL

Target Audiences

Beginner Data Analysts or students who want to start with Machine Learning using just SQL

The goal of this course is to learn how to create and use Machine Learning models right from the level of SQL query in Google BigQuery interface. You will also learn how to prepare data, how to interpret model results and how to make nice predictions using just one SELECT statement. You will work on a real data set – car sale offers in the USA, and the goal will be to predict the price of a car.

The course consists of 7 sections and one bonus section. At the very beginning we will create an environment to work in. Next it would be good to see a little theory. Then we will straight jump into the first model creation. In further lessons we will try to improve our model performance by some hacks and tricks. This is essential for the course and we put the biggest pressure on that part. In the meantime you will get all needed resources and you will be able to practice all steps by yourself on your own free BigQuery account.

In this course you will be working on your own end project. During the course, we will guide you on how to make every step of your own end project. After each practical lesson, you will have a homework assignment that will contribute to your big project. The project’s goal is to predict used car prices. Additionally, to motivate you to work and check if you have done your homework correctly, you will get a question in the quiz. By carrying out practical tasks, you will easily find answers.

We’ve added a few lesson resources. Google glossary ebook that explains all basic definitions of a wide spectrum of Machine Learning. Please read them to systematize your knowledge. Other resources are cheat sheets which present a summary for each topic. It’s a really nice source of condensed knowledge. Please use them to quickly look if you forgot some stuff. For practice lessons we add our SQL in resources. You can easily copy-paste and manipulate the code by yourself.

Let’s get started with our journey of Machine Learning in SQL!

Course Curriculum

Chapter 1: Before start the Course

Lecture 1: Lesson 0.1 Course Introduction

Lecture 2: Lesson 0.2 First Thing To Do

Lecture 3: Lesson 0.3 Setting up BigQuery Sandbox

Chapter 2: Introduction – basic concepts and theory

Lecture 1: Lesson 1.1 What is Machine Learning?

Lecture 2: Lesson 1.2 What is Linear Regression?

Lecture 3: Lesson 1.3 What is Google Cloud Platform and BigQuery?

Lecture 4: Lesson 1.4 What is BigQuery ML?

Lecture 5: Lesson 1.5 BigQuery Data types

Lecture 6: Lesson 1.6 BigQuery SQL Fundamentals

Chapter 3: Creating first model and prediction

Lecture 1: Lesson 2.0 Section introduction

Lecture 2: Lesson 2.1 Business goal and model limitation

Lecture 3: Lesson 2.2 Data source description

Lecture 4: Lesson 2.3 BigQuery User Interface

Lecture 5: Lesson 2.4 Import data to BigQuery

Lecture 6: Lesson 2.5 Create model

Lecture 7: Lesson 2.6 Predict data

Lecture 8: Lesson 2.7 Model evaluation

Chapter 4: Data cleaning

Lecture 1: Lesson 3.0 Section Introduction

Lecture 2: Lesson 3.1 Removing useless columns

Lecture 3: Lesson 3.2 Data visualization with Google Data Studio

Lecture 4: Lesson 3.3 Histogram

Lecture 5: Lesson 3.4 Checking duplicates

Lecture 6: Lesson 3.5 Removing null values

Chapter 5: Feature engineering

Lecture 1: Lesson 4.0 Section introduction

Lecture 2: Lesson 4.1 Create new feature – car age

Lecture 3: Lesson 4.2 Create new feature – VIN number

Lecture 4: Lesson 4.3 Create new feature – Condition field

Lecture 5: Lesson 4.4 Create new feature – Model field

Lecture 6: Lesson 4.5 Create new feature – Geography

Chapter 6: Feature engineering – built-in function

Lecture 1: Lesson 5.0 Section introduction

Lecture 2: Lesson 5.1 ML.MIN_MAX_SCALER function

Lecture 3: Lesson 5.2 ML.FEATURE_CROSS function

Lecture 4: Lesson 5.3 ML.POLYNOMIAL_EXPAND function

Lecture 5: Lesson 5.4 ML.QUANTILE_BUCKETIZE function

Lecture 6: Lesson 5.5 ML.BUCKETIZE function

Lecture 7: Lesson 5.6 ML.NGRAMS function

Lecture 8: Lesson 5.7 Removing unimportant columns

Chapter 7: Hyperparameters tuning

Lecture 1: Lesson 6.0 Section introduction

Lecture 2: Lesson 6.1 L1 & L2 regularization

Lecture 3: Lesson 6.2 Automatic vs manual tuning

Instructors

Brain Analytix
Data Scientist & Business Intelligence Developer
Michał Kałucki

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 13 votes

Frequently Asked Questions

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