fix: fixed notebook links and movd numpy article to blog

Atlas/Engineering/machine-learning/machine-learning-roadmap.md

@@ -11,8 +11,8 @@ This is a learning roadmap for anyone who want to be a ML Engineer. I created it
 
 ### Topics
 
-- [Python basics (variables, functions, loops, classes, data structures, testing)](./level-0-python-and-math-essentials/python-tutorial-notes.ipynb) ✅
-- [NumPy](./level-0-python-and-math-essentials/numpy/numpy-essentials.md) ✅
+- [Python basics (variables, functions, loops, classes, data structures, testing)](https://github.com/Frodigo/garage/blob/main/Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/python-tutorial-notes.ipynb) ✅
+- [NumPy](https://github.com/Frodigo/garage/blob/main/Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/numpy/numpy-essentials.ipynb) ✅
 - pandas
 - Matplotlib & Seaborn for visualization
 - Basic linear algebra, probability, statistics

Blog.md

@@ -1,5 +1,6 @@
 ## 2025
 
+- [Numpy essentials I learned as beginner](Numpy%20essentials%20I%20learned%20as%20beginner.md)
 - [Configuring AI coding tools is a new bike-shedding](Configuring%20AI%20coding%20tools%20is%20a%20new%20bike-shedding.md)
 - [Omarchy review after a month of using](Omarchy%20review%20after%20a%20month%20of%20using.md)
 - [Setting up Nextcloud on Raspberry Pi 5 using Docker](Setting%20up%20Nextcloud%20on%20Raspberry%20Pi%205%20using%20Docker.md)
@@ -15,21 +16,24 @@
 - [How saying ‘I don’t know’ can make You a better Software Engineer](How%20saying%20‘I%20don’t%20know’%20can%20make%20You%20a%20better%20Software%20Engineer.md)
 
 ---
+
 ## 2024
 
 - [Monolith vs Microservices - Pros and Cons and How to Approach Transformation](Monolith%20vs%20Microservices%20-%20Pros%20and%20Cons%20and%20How%20to%20Approach%20Transformation.md)
 
 ---
+
 ## 2023
 
 - [Vue component here, Vue components there. Components everywhere!](Vue%20component%20here,%20Vue%20components%20there.%20Components%20everywhere!.md)
-- [How to get started with Vue (part1)](How%20to%20get%20started%20with%20Vue%20(part1).md)
-- [Hexagonal architecture (ports and adapters) - pros and cons](Hexagonal%20architecture%20(ports%20and%20adapters)%20-%20pros%20and%20cons.md)
+- [How to get started with Vue (part1)](<How%20to%20get%20started%20with%20Vue%20(part1).md>)
+- [Hexagonal architecture (ports and adapters) - pros and cons](<Hexagonal%20architecture%20(ports%20and%20adapters)%20-%20pros%20and%20cons.md>)
 - [Magento GraphQL - How to resolve URL](Magento%20GraphQL%20-%20How%20to%20resolve%20URL.md)
 - [How to import Magento GraphQL schema to Postman](How%20to%20import%20Magento%20GraphQL%20schema%20to%20Postman.md)
 - [How to build a high-quality PWA Studio extension](How%20to%20build%20a%20high-quality%20PWA%20Studio%20extension.md)
 
 ---
+
 ## 2022
 
 - [2 ways of handling GraphQL errors in Apollo Client](2%20ways%20of%20handling%20GraphQL%20errors%20in%20Apollo%20Client.md)
@@ -43,6 +47,7 @@
 - [10 Answers to Your Questions About Magento Enterprise Versions](10%20Answers%20to%20Your%20Questions%20About%20Magento%20Enterprise%20Versions.md)
 
 ---
+
 ## 2021
 
 - [What is JSX in React, and is it worth making friends with it](What%20is%20JSX%20in%20React,%20and%20is%20it%20worth%20making%20friends%20with%20it.md)

Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/numpy/numpy-essentials.md → Blog/2025/Numpy essentials I learned as beginner.md

@@ -1,10 +1,11 @@
 ---
 title: NumPy Essentials
 date: 2025-11-07
-permalink: atlas/engineering/machine-learning/learning-roadmap/numpy-essentials
+permalink: numpy-essentials-i-learned-as-a-beginner
 ---
+I’m learning machine learning and I’m at level 0, just getting started. Last week, I was exploring things related to NumPy, and the result of my learning is a Jupyter notebook, which I wanted to share with you.
 
-# NumPy Essentials
+Note: You can find [Jupyter Notebook source here](https://github.com/Frodigo/garage/blob/main/Atlas/Engineering/machine-learning/level-0-python-and-math-essentials/numpy/numpy-essentials.ipynb) (On blog you can see this notebook converted to text).
 
 NumPy (Numerical Python) is a Python library that makes working with numbers faster and easier. It offers new datatype: ndarray, vectorization and bunch of useful functions. NumPy is a tool that every ML or Data Engineer use daily.In this Notebook, I am going through NumPy features and show examples how it can be used.
 
@@ -752,6 +753,8 @@ In this notebook, we've covered some essential and practical aspects of using Nu
 - Useful attributes such as `.shape` and `.dtype`
 - Conversion between 1D and 2D arrays and understanding reshape behavior
 
+I spent approximatelly 1 hour every day in a week to learn, play, expermiment and write this notebook. If I did it, you can too!
+
 ## Topics for Further Learning
 
 If you wish to deepen your NumPy skills, consider exploring the following: