Logistic Regression clearly explained 👇🏻 1️⃣ 𝗟𝗢𝗚𝗜𝗦𝗧𝗜𝗖 𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
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.

Linear Regression clearly explained 👇🏻 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.

🚀 New to SQL?

Here’s a roadmap to get you started, step-by-step! 🧵👇

(Don't forget to bookmark for later!! 😉 ) 1. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀
Before diving deep, it's essential to grasp what is SQL.

Begin with:
• What is a Relational Database?
• Comparing SQL and NoSQL Databases
• Understanding SQL Syntax and Basic Commands

How Python work in our computer clearly explained👇🏻 1️⃣ 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝗵𝗲𝗹𝘃𝗲𝘀: 𝗪𝗵𝗲𝗿𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝘁𝗼𝗿𝗲𝘀 𝗗𝗮𝘁𝗮
Imagine your computer's memory as a vast warehouse of shelves.

Each byte is a slot on these shelves.

Python, with your OS's permission, uses this space for its data and code. 🏢

Today let's learn SQL's execution order and its importance 👇🏻 1️⃣ 𝗦𝗤𝗟 𝗔𝗦 𝗔 𝗗𝗘𝗖𝗟𝗔𝗥𝗔𝗧𝗜𝗩𝗘 𝗟𝗔𝗡𝗚𝗨𝗔𝗚𝗘
𝘠𝘰𝘶 𝘵𝘦𝘭𝘭 𝘪𝘵 𝘸𝘩𝘢𝘵 𝘺𝘰𝘶 𝘸𝘢𝘯𝘵, 𝘯𝘰𝘵 𝘩𝘰𝘸 𝘵𝘰 𝘥𝘰 𝘪𝘵.

So SQL expects statements to be written in a specific order ...

but their evaluation sequence differs.

SQL Joins clearly explained 👇🏻 1️⃣ 𝗟𝗲𝗳𝘁 𝗝𝗼𝗶𝗻
Left Join returns all records from the left table (A) and the matched records from the right table (B). If there's no match, the result is NULL on the right side.

💡Think of it as keeping all items in A and filling gaps from B where they exist.

Are you the one crafting charts to communicate your data?

Then you better stay with me! 🤓

Today let's dive into visual encoding and their key role in DataViz! 👇🏻 Charts are composed of two core components:
- Data
- Visuals

But how do we convert this data into a visual representation?

Say hi to visual encodings! 👋🏻

Struggling to craft effective charts? 🤔📊

Let's understand 𝘄𝗵𝘆 some charts are 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲 👇🏻 Think of DataViz as your GPS in the world of numbers.

It turns complex data into clear, actionable insights.

But... why do some charts enlighten us while others don't?

The Gestalt Theory explains how our brains perceive patterns and how to take advantage of them.

The Transformer's encoder clearly explained 👇🏻 1️⃣ 𝗪𝗛𝗔𝗧'𝗦 𝗧𝗛𝗘 𝗘𝗡𝗖𝗢𝗗𝗘𝗥? 🧠

The Encoder is the part responsible for processing input tokens through self-attention and feed-forward layers to generate context-aware representations.

👉 It’s the powerhouse behind understanding sequences in NLP models.

Struggling to craft clear charts? 🤔📊

Let's understand why some charts are misleading and how to avoid it 👇🏻 First things first, ever heard of the term "ink" in data visualization?

It's not about the ink in your pen.

It refers to any part of a visualization that stands out from the background.

Think lines, bars, points, and even text. 🖋️

How to deal with outliers clearly explained 👇🏻 1️⃣ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗼𝘂𝘁𝗹𝗶𝗲𝗿?
Outliers are data points that are very different from the rest of the dataset. They are unrepresentative of the overall data. 🚨📉

Ever felt confused by SQL's execution flow? 🤔

Then you better stay with me!

Today let's exemplify SQL's execution order with a simple query👇🏻 1️⃣ 𝗦𝗧𝗔𝗥𝗧𝗜𝗡𝗚 𝗙𝗥𝗢𝗠 𝗢𝗨𝗥 𝗥𝗔𝗪 𝗧𝗔𝗕𝗟𝗘
We use a dummy table with the salary of employees depending on their field and experience,

🎯 Our main goal?
Understand the field that earns the most.

The Transformer's decoder clearly explained 👇🏻 1️⃣ WHAT'S THE DECODER? 🎯
The Decoder is the brain behind transforming encoded inputs and previously generated tokens into context-aware outputs.

Imagine it as the artist who paints the final picture from sketches. 🖌️

The Transformers architecture clearly explained 👇🏻 Today I'm starting a new series of threads to simplify the concept of Transformers and what's behind the Natural Language abilities of LLMs.

Let's start with the basics of the Transformer architecture:

The encoder/decoder concept. 🧠✨

Support Vector Machine exemplified for dummies 👇🏻

(Don't forget to save for later 😉) 1️⃣ GETTING THE DATA
Just like stargazing, we begin by gathering our cosmic data.

Thanks to Kaggle, we have real-world data on pulsar stars.

Now, let's decode this celestial Morse code to predict a star's pulse! 💫🔍

The Transformer's decoder clearly explained 👇🏻 1️⃣ WHAT'S THE DECODER? 🎯
The Decoder is the brain behind transforming encoded inputs and previously generated tokens into context-aware outputs.

Imagine it as the artist who paints the final picture from sketches. 🖌️

The Transformer's encoder clearly explained 👇🏻 1️⃣ 𝗪𝗛𝗔𝗧'𝗦 𝗧𝗛𝗘 𝗘𝗡𝗖𝗢𝗗𝗘𝗥? 🧠

The Encoder is the part responsible for processing input tokens through self-attention and feed-forward layers to generate context-aware representations.

👉 It’s the powerhouse behind understanding sequences in NLP models.

Multiple-class Logistic Regression exemplified for dummies 👇🏻 1️⃣ 𝗗𝗔𝗧𝗔 𝗚𝗔𝗧𝗛𝗘𝗥𝗜𝗡𝗚 𝗣𝗛𝗔𝗦𝗘

We're using Kaggle to find robust data for our flippered friends.

Meet our stars:
- Iris Setosa
- Iris Versicolor
- Iris Virginica

Like biologists in the wild, we'll use data points to distinguish between them.

Multiple-class Logistic Regression clearly explained 👇🏻 By default, Logistic Regression is like a coin toss - heads or tails, A or B.

But what if you have multiple classes?

That's where we adapt our model for MULTIPLE CHOICES!
There are two main ways:

The Transformers architecture clearly explained 👇🏻 Today I'm starting a new series of threads to simplify the concept of Transformers and what's behind the Natural Language abilities of LLMs.

Let's start with the basics of the Transformer architecture:

The encoder/decoder concept. 🧠✨

Today I am bringing how JOINs work in both SQL and Python👇🏻 𝗦𝗤𝗟 𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗰𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝘁 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀.

However, there are cases in which the shift towards Python and Pandas is undeniable.

Why?

Pandas can do everything SQL can, and way more.

So let's see how to pass from SQL syntax to Python.