Create Table Of Superheroes
- Create a table called SUPERHEROES. If it already exists, replace it. CREATE OR REPLACE TABLE SUPERHEROES ( - Column called ID allowing up to five characters "ID" VARCHAR(5), - Column called NAME allowing up to 100 characters "NAME" VARCHAR(100), - Column called ALTER_EGO allowing up to 100 characters "ALTER_EGO" VARCHAR(100), - Column called BANK_BALANCE allowing 38 digits with 2 after the decimal point "BANK_BALANCE" NUMBER(38, 2) );
Insert Rows For Each Superhero
- Insert rows into SUPERHEROES INSERT INTO SUPERHEROES - With the values VALUES ('XF6K4', 'Chris Maki', 'The Bomber', '-100.20'), ('KD5SK', 'Donny Mav', 'Nuke Miner', '200.30');
View Table Of Superheroes
- View the table SELECT * FROM SUPERHEROES;
|XF6K4||Chris Maki||The Bomber||-100.20|
|KD5SK||Donny Mav||Nuke Miner||200.30|
Create Table Of Superhero Powers
- Create a table called POWERS. If it already exists, replace it. CREATE OR REPLACE TABLE POWERS ( - Column called ID allowing up to five characters "ID" VARCHAR(5), - Column called SUPER_POWER allowing up to 100 characters "SUPER_POWER" VARCHAR(100) );
Insert Rows For Each Superpower
- Insert rows into POWERS INSERT INTO POWERS - With the values VALUES ('XF6K4', 'invisibility'), ('KD5SK', 'fire blast'), ('TKSI1', 'mind control') ;
Merge Superhero And Superpower Tables
Notice that after this merge there are two
ID columns. This is because both tables contained an ID column and therefore they both will appear in the final table.
- Select all columns SELECT * - From SUPERHEROES table FROM SUPERHEROES - After left joining in the POWERS table LEFT JOIN POWERS - Where the ID's in both tables are the same ON SUPERHEROES.ID = POWERS.ID ;
|XF6K4||Chris Maki||The Bomber||-100.20||XF6K4||invisibility|
|KD5SK||Donny Mav||Nuke Miner||200.30||KD5SK||fire blast|
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
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