Google BigQuery ML Machine Learning in SQL (without Python)

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Brain Analytix,Michał Kałucki

3:17:46

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1 - Before start the Course

1 - Lesson01CourseIntroduction.pdf

1 - Lesson 01 Course Introduction.mp4

03:59

2 - Lesson02FirstThingToDo.pdf

2 - Lesson 02 First Thing To Do.mp4

01:51

3 - Lesson03SettingupBigQuerySandboxaccount.pdf

3 - Lesson 03 Setting up BigQuery Sandbox.mp4

03:06

2 - Introduction basic concepts and theory

1 - Lesson 11.html

2 - Lesson 12.html

3 - Lesson 13.html

4 - AlgorythmsCheetsheet.pdf

4 - AlgorythmsProsandConsCheetsheet.pdf

4 - GoogleMachineLearningGlossary.pdf

4 - Lesson11WhatisMachineLearning.pdf

4 - Lesson 11 What is Machine Learning.mp4

05:21

4 - Lesson 14.html

5 - Lesson12WhatisLinearRegression.pdf

5 - Lesson 12 What is Linear Regression.mp4

04:01

5 - Lesson 15.html

6 - BigQueryCheatSheet.pdf

6 - Lesson13WhatisGoogleCloudPlatformandBigQuery.pdf

6 - Lesson 13 What is Google Cloud Platform and BigQuery.mp4

06:38

6 - Lesson 16.html

7 - Lesson14WhatisBigQueryML.pdf

7 - Lesson 14 What is BigQuery ML.mp4

04:51

8 - Lesson15BigQuerySQLDatatypes.pdf

8 - Lesson 15 BigQuery Data types.mp4

03:57

9 - Lesson16BigQuerySQLFundamentals.pdf

9 - Lesson 16 BigQuery SQL Fundamentals.mp4

06:56

3 - Creating first model and prediction

7 - Lesson 21.html

8 - Lesson 22.html

9 - Lesson 23.html

10 - Lesson 20 Section introduction.mp4

01:15

10 - Lesson 25.html

11 - Lesson21Businessgoalandmodellimitation.pdf

11 - Lesson 21 Business goal and model limitation.mp4

04:10

11 - Lesson 26.html

12 - Lesson22Datasuorcedescription.pdf

12 - Lesson 22 Data source description.mp4

05:24

12 - Lesson 27.html

13 - Lesson 23 BigQuery User Interface.mp4

05:29

14 - Lesson 24 Import data to BigQuery.mp4

05:27

15 - Lesson25SQLCode.txt

15 - Lesson 25 Create model.mp4

04:35

16 - Lesson26SQLCode.txt

16 - Lesson 26 Predict data.mp4

03:29

17 - Lesson27Modelevaluation.pdf

17 - Lesson 27 Model evaluation.mp4

07:27

4 - Data cleaning

13 - Lesson 31.html

14 - Lesson 32.html

15 - Lesson 33.html

16 - Lesson 34.html

17 - Lesson 35.html

18 - Lesson 30 Section Introduction.mp4

01:37

19 - Lesson 31 Removing useless columns.mp4

04:15

20 - Lesson 32 Data visualization with Google Data Studio.mp4

06:49

21 - Lesson33Histogram.pdf

21 - Lesson33SQLCode.txt

21 - Lesson 33 Histogram.mp4

08:57

22 - Lesson34Checkingduplicates.pdf

22 - Lesson34SQLCode.txt

22 - Lesson 34 Checking duplicates.mp4

04:34

23 - Lesson35Removingnullvalues.pdf

23 - Lesson35SQLCode.txt

23 - Lesson 35 Removing null values.mp4

03:39

5 - Feature engineering

18 - Lesson 41.html

19 - Lesson 42.html

20 - Lesson 43.html

21 - Lesson 44.html

22 - Lesson 45.html

24 - Lesson 40 Section introduction.mp4

01:40

25 - Lesson41Createnewfeaturecarage.pdf

25 - Lesson41SQLCode.txt

25 - Lesson 41 Create new feature car age.mp4

05:17

26 - Lesson42CreatenewfeatureVINnumber.pdf

26 - Lesson42SQLCode.txt

26 - Lesson 42 Create new feature VIN number.mp4

03:35

27 - Lesson43CreatenewfeatureConditioncardinalorder.pdf

27 - Lesson43SQLCode.txt

27 - Lesson 43 Create new feature Condition field.mp4

04:28

28 - Lesson44CreatenewfeaturereduceModelcases.pdf

28 - Lesson44SQLCode.txt

28 - Lesson 44 Create new feature Model field.mp4

04:35

29 - Lesson45Createnewfeaturedistancefromcapitals.pdf

29 - Lesson45SQLCode.txt

29 - Lesson 45 Create new feature Geography.mp4

06:12

6 - Feature engineering builtin function

23 - Lesson 51.html

24 - Lesson 52.html

25 - Lesson 53.html

26 - Lesson 54.html

27 - Lesson 55.html

28 - Lesson 56.html

29 - Lesson 57.html

30 - Lesson 50 Section introduction.mp4

01:39

31 - Lesson51MLMINMAXSCALERfunction.pdf

31 - Lesson51SQLCode.txt

31 - Lesson 51 MLMINMAXSCALER function.mp4

03:48

32 - Lesson52SQLCode.txt

32 - Lesson 52 MLFEATURECROSS function.mp4

03:20

33 - Lesson53SQLCode.txt

33 - Lesson 53 MLPOLYNOMIALEXPAND function.mp4

02:40

34 - Lesson54SQLCode.txt

34 - Lesson 54 MLQUANTILEBUCKETIZE function.mp4

03:16

35 - Lesson55SQLCode.txt

35 - Lesson 55 MLBUCKETIZE function.mp4

02:46

36 - Lesson56SQLCode.txt

36 - Lesson 56 MLNGRAMS function.mp4

02:42

37 - Lesson57SQLCode.txt

37 - Lesson 57 Removing unimportant columns.mp4

05:11

7 - Hyperparameters tuning

30 - Lesson 61.html

31 - Lesson 62.html

38 - Lesson 60 Section introduction.mp4

01:30

39 - Lesson61L1L2regularization.pdf

39 - Lesson61SQLCode.txt

39 - Lesson 61 L1 & L2 regularization.mp4

06:51

40 - Lesson62Automaticvsmanualtuning.pdf

40 - Lesson62SQLCode.txt

40 - Lesson 62 Automatic vs manual tuning.mp4

03:52

8 - Final prediction and model testing

32 - Lesson 71.html

33 - Lesson 72.html

34 - Lesson 73.html

41 - Lesson 70 Section introduction.mp4

01:31

42 - Lesson71SQLCode.txt

42 - Lesson 71 Negative price.mp4

04:12

43 - Lesson72SQLCode.txt

43 - Lesson 72 Using logarithm function.mp4

03:14

44 - Lesson73SQLCode.txt

44 - Lesson 73 Test model quality.mp4

05:33

45 - Lesson 74 Final lesson.mp4

04:29

9 - Extra Section Boosted Tree Algorithm

35 - Extra Lesson 1.html

36 - Extra Lesson 2.html

46 - Extra Lesson 0 Introduction.mp4

01:13

47 - BonusLesson1.pdf

47 - Extra Lesson 1 Boosted Tree short theory.mp4

02:33

48 - Extralesson2SQLCode.txt

48 - Extra Lesson 2 Model train and predict.mp4

03:52

Description

On Linear Regression example

What You'll 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 is this for?

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

What You Need to Know?

Basic knowledge of SQL

More details

Description
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 projectâ€™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.

Weâ€™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.

Letâ€™s get started with our journey of Machine Learning in SQL!
Who this course is for:
Beginner Data Analysts or students who want to start with Machine Learning using just SQL

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We are friends with almost 10 year experience in Data Science and Business Intelligence. We have worked in many different companies and industries like: Electronics Manufacturing, Civil Engineering, Gaming, Gambling and Projects Management. We are familiar with wide range of data visualization tools: Tableau, Power BI, Google Data Studio, Grafana and Qlik. We have knowledge of language programming in Python, R, DAX and SQL. Our main skills are data analysis, machine learning, data engineering and data visualization.

Michał Kałucki

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