Rounding it means in pandas formating like f .2

.

Rand()

LIKE

Sure! Let's simplify it:

1. Supervised Learning: It's like teaching your dog tricks by showing it what to do and giving it a treat when it gets it right. For example, if you show your dog pictures of cats and dogs and tell it which is which, it will learn to recognize them.

2. Unsupervised Learning: Imagine you have a big box of toys, and you don't know which ones are similar. Unsupervised learning is like sorting those toys into groups based on how they look or what they do without anyone telling you how to group them.

3. Semi-Supervised Learning: This is like when your teacher gives you some homework with answers and some without. You learn from the homework with answers, and then you try to figure out the rest on your own.

4. Reinforcement Learning: Picture yourself playing a video game. You try different moves, and when you do something good, you get points or rewards. Reinforcement learning is like teaching a computer to play a game by giving it points for doing the right things.

5. Deep Learning: Imagine you have lots of LEGO blocks, and you want to build a really cool spaceship. Deep learning is like having instructions to build the spaceship layer by layer. Each layer adds more details until you have an awesome spaceship.

So, in simple terms:
- Supervised learning is like teaching with examples and rewards.
- Unsupervised learning is like sorting things out without being told how.
- Semi-supervised learning is like doing homework with some answers and some without.
- Reinforcement learning is like learning by getting points for doing good things.
- Deep learning is like building something step by step with lots of tiny pieces.

ML

aggfunc

SELECT FLOOR(1 + RAND() * 10) AS random_integer;


RAND()

SELECT * FROM table_name WHERE RAND() <= 0.1;

SELECT * FROM table_name ORDER BY RAND();

LIKE %

re.findall('[\w+]+@[\w\.]+', random_text)

how to use re to list comprehension