Ever felt lost in Python's universe of data types? ๐
Then dive into the basics with me today!
Today we're exploring Booleans, Integers, and Floats โ the core elements in Python's data type galaxy! ๐๐
These data types are like the atoms of Python.
We start with them individually and later combine them into larger structures like lists and dictionaries.
It's a journey from simplicity to complexity!
1๏ธโฃ ๐๐ข๐ข๐๐๐๐ก๐ฆ
Booleans are the on/off switches of Python ๐ฆ.
Booleans decide your code's route, guiding it with simple yet powerful TRUE or FALSE signals.
They're the silent guardians of logic!
2๏ธโฃ ๐๐ก๐ง๐๐๐๐ฅ๐ฆ
Integers are the digital legos in Python ๐งฑ
From 0 to infinity...
they're the countable stones that pave the path of loops and arrays in your code.
3๏ธโฃ ๐๐๐ข๐๐ง๐ฆ
Floats are the Artists of Precision ๐จ
They're the precision points that give your calculations depth and detail, just like the delicate brush strokes on a canvas.
๐ผ ๐๐ฎ๐ฟ๐บ๐ผ๐ป๐ถ๐๐ถ๐ป๐ด ๐ก๐๐บ๐ฏ๐ฒ๐ฟ๐ - ๐ง๐ต๐ฒ ๐๐ป๐๐ฒ๐ด๐ฒ๐ฟ-๐๐น๐ผ๐ฎ๐ ๐๐ฎ๐น๐น๐ฒ๐
Mixing Integers and Floats in Python is like a duet in music ๐ถ
Python conducts the harmony, elevating Integers to Floats for a symphony of precise computations.
4๏ธโฃ ๐๐ข๐ก๐ฉ๐๐ฅ๐ง๐๐ก๐ ๐๐๐ง๐ ๐ง๐ฌ๐ฃ๐๐ฆ
Transforming data types in Python is like a wizard's spell ๐งโโ๏ธ.
Changing data types in Python is like casting a spell. With a flick of a function, watch a Boolean transform into an Integer or a Float!
Understanding Python's data types is like mastering the alphabet of a new language. ๐
It empowers you to write code that's clear, efficient, and impactful.
Ready to experiment with these fundamental types?
And this is all for now! ๐ค
Did you like the thread?
Then join my freshly started DataBites newsletter to get all my content right to your mail every Sunday! ๐งฉ
Simple Linear Regression exemplified for dummies๐๐ป
(Don't forget to bookmark for later! ๐)
1๏ธโฃ ๐๐๐ง๐ ๐๐๐ง๐๐๐ฅ๐๐ก๐ ๐ฃ๐๐๐ฆ๐
We're using height and weight - a classic duo often assumed to have a linear relationship.
But assumptions in data science? No way! ๐ง
Let's find out:
- Do height and weight really share a linear bond?
Do you like this post?
Then join my DataBites newsletter to get all my content right to your mail every Sunday! ๐งฉ
Linear regression is the simplest statistical regression method used for predictive analysis.
It can be performed with multiple variables.... but today we'll focus on a single one.
Also known as Simple Linear Regression.
1๏ธโฃ ๐ฆ๐๐ ๐ฃ๐๐ ๐๐๐ก๐๐๐ฅ ๐ฅ๐๐๐ฅ๐๐ฆ๐ฆ๐๐ข๐ก
In Simple Linear Regression, we use one independent variable to predict a dependent one.
The main goal? ๐ฏ
Finding a line of best fit.
It's simple yet powerful, revealing hidden trends in data.
Linear regression is the simplest statistical regression method used for predictive analysis.
It can be performed with multiple variables.... but today we'll focus on a single one.
Also known as Simple Linear Regression.
1๏ธโฃ ๐ฆ๐๐ ๐ฃ๐๐ ๐๐๐ก๐๐๐ฅ ๐ฅ๐๐๐ฅ๐๐ฆ๐ฆ๐๐ข๐ก
In Simple Linear Regression, we use one independent variable to predict a dependent one.
The main goal? ๐ฏ
Finding a line of best fit.
It's simple yet powerful, revealing hidden trends in data.
The Encoder is the part responsible for processing input tokens through self-attention and feed-forward layers to generate context-aware representations.
๐ Itโs the powerhouse behind understanding sequences in NLP models.
Are you enjoying this post?
Then join my newsletter DataBites to get all my content right to your mail every week! ๐งฉ
Logistic Regression clearly explained with visuals๐๐ป
(Don't forget to bookmark for later! ๐)
1๏ธโฃ ๐๐ข๐๐๐ฆ๐ง๐๐ ๐ฅ๐๐๐ฅ๐๐ฆ๐ฆ๐๐ข๐ก
It is a binary classification model used to classify our input data into two main categories.
It can be extended to multiple classifications... but today we'll focus on a binary one.
Also known as Simple Logistic Regression.
2๏ธโฃ ๐๐ข๐ช ๐ง๐ข ๐๐ข๐ ๐ฃ๐จ๐ง๐ ๐๐ง?
The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.
It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.