I've brought you some amazing GitHub repositories of web development tips and tricks 🌟

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A complete beginner's guide to CSS Grid layout 👇🏻

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Grid is used for making complex web design layouts more easily as it's not so hard to master

Using Flex you can make only 1D layout but Grid gives you full power of creating 2D layout

Let's start

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First things first, start with giving the display property "grid" to the container element or parent element

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Nothing will change after adding display: flex; in the parent container becuase we need to define the width of columns. In order to set that columns width we have gird-template-columns property

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Let's start with defining the width of our columns.

For example, let's say I need two columns of width 60% and 40% respectively

grid-template-columns: 60% 40%;

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Neumorphism is a trendy UI style nowadays. So let's create a neumorphic form using few simple steps

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Neumorphic design is another UI design that has become popular these days.

Making neumorphic effect is actually pretty easy using only just CSS box-shadow

We'll be creating a neumorphic form in steps👇🏼

2.

STEP 1:

Place a light source(virtually) on screen. In this example I'm considering that my light source is placed top left corner of the screen

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STEP 2:

Create a container div and set background color slightly darker than white. I picked #ecf0f3

* Make sure that the color of body and container should be same

CONTINUE...

4.

As we placed our light source on top-left corner, make sure left and top border of container should be brighter than right and bottom border.

This will make the element looks like it has the light source illuminating from the top left corner of the screen.

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Finding good courses is challenging.

Here are five courses on JavaScript, Python, and Go you can consider in 2022.

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1️⃣ Basic JavaScript

Interactive learning will help you understand the basic concepts of JavaScript quickly.

• Introduction
• Variables
• Comparisons
• Loops

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2️⃣ Advance JavaScript

Understanding complex concepts of JavaScript is the with exercises and challenges.

• Functions
• Arrays
• Objects

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3️⃣ Python

You'll get hands-on practice with all the building blocks to ensure you excel as a Pythonista.

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4️⃣ DS and Algo in Python

Algorithms are tough to master. But this course made it easy with code samples and quizzes.

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You are looking to get into Machine Learning? You most certainly can
Because I believe that if an above-average student like me was able to do it, you all certainly can as well

Here's how I went from knowing nothing about programming to someone working in Data Science👇

The path that I took wasn't the most optimal way to get a good grip on Machine Learning because...

when I started out, I knew nobody that worked or had knowledge of Data Science which made me try all sorts of different things that were not actually necessary.

I studied C programming as my first language during my freshman year in college. And before the start of my second year, I started learning python just because I knew C is not the way to go.
I learned it out of curiosity and I had no idea about Machine Learning at this point.

I did not learn python by courses but by books. I'll link all the resources at the end.
The approach I took was just to make the same kind of programs I made in C but just replacing the syntax with that of python and practised those.
Already knowing a language made it easier.

For Machine Learning, the first thing that I did was to join Andrew NG's course which really hit it off for me
I didn't have to have any idea about Machine Learning for the course. I completed that in almost a month and it gave me a good intuition of things and the flow of ML.

"I want to learn how to code, but have no idea where to begin."

I've been getting this question pretty frequently lately, and thought it was a good time to put together a thread to cover all the basics.

And here it is!

Instead of just throwing a few links at you, I first want to cover something much more fundamental: The *reason* you want to learn to code.

I've had to train a good number of developers in my time. The ones that succeeded had rock-solid, persistent motivations. Is that you?

Some good motivations for learning how to code:

* Enabling a career change
* Insatiable curiosity, eagerness to learn
* Frustration with inefficiency + desire to improve things
* Larger goals in mind, coding being a stepping stone

Some bad motivations for learning how to code:

* It seems popular
* Your friends/family have ideas for apps
* You want to make lots of money by sitting in front of a computer
* You bought an expensive MacBook and are struggling to justify the expense

For those, trade forex.

Motivation matters, because of the one thing that's universally true about coding: It's hard.

Depending on what you do, you may have to learn several languages at once. Tools constantly update. New approaches land in the market on an annual basis. You're constantly upskilling.

𝐆𝐢𝐭𝐇𝐮𝐛 serves more than 73,000,000 developers.

These repositories are so priceless 🤯 , better than some courses out there ✨

Here are 10 repositories that you don't wanna miss.

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🞖 Clean code JavaScript 🞖

This repository takes the concepts of clean code by Mr. Robert C. Martin and adapts them for JavaScript so you can use them in future

🞖 Free-for. dev 🞖

This repository consists of a list of software(Saas, Paas, Iaas, etc.) and other offerings that have free tiers for

🞖 Tech Interview Handbook. 🞖

This repository can be used by anybody who wants to land a job at a tech company but is new to technical

🞖 JavaScript-Algorithms 🞖

This repository contains JavaScript-based examples of many popular algorithms and data

Do you want to learn AWS (or an experienced AWS dev) ?

Here's a list of Github repositories you should check out:

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1. Serverless:

Serverless Framework – Build web, mobile and IoT applications with serverless architectures using AWS Lambda, Azure Functions, Google CloudFunctions &

2. Localstack:

💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps

3. data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data, matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command

4. awesome-kubernetes:

A curated list for awesome kubernetes