Basics Of HTML

Linux File System in 1 Page

Which Jobs AI Can't Replace?! 😶‍🌫️

https://techurl.in/dIJaG

Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?

Here's a concise breakdown of each, designed to clarify your options:

### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.

Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer

### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.

Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator

### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.

Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher

### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.

Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst

### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.

Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.

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Python Cheat Sheet


https://x.com/tech_psyche/status/1846295907458777552

Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run

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You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

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DESIGNERS
Master the Art of Color Blending with these Resources.

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How to Land a Software Development Role in Top Tech Companies.

🥳
Dreaming of landing a software development role at top tech companies? Your journey to Silicon Valley’s elite starts here!
Whether you're a coding wizard or just getting started, this guide offers insider tips that will get you noticed by industry giants like Google, Amazon, and Apple. Ready to unlock the door to your dream job?

Check Out Here: https://techurl.in/lbVVO

Your biggest enemy 𝐅𝐄𝐀𝐑 𝐨𝐟 𝐑𝐞𝐣𝐞𝐜𝐭𝐢𝐨𝐧

People hesitate to apply for many opportunities just because of fear of rejection.

However, not applying means you are automatically rejecting yourself. They usually think I will start applying after 6-8 months with full preparation.

Do you really think it will work ??? Interview calls usually take months 😅

My suggestion would be to start applying after 10 days to 1 month of preparation . Try to give as many interviews as you can. In this way, you will learn 👇🏻

🌴 Frequently asked questions
🌴 Interview pattern
🌴 How to tweak your answers?

Give a try ,even in the worst scenario, you will get some interview experience. That experience will eventually help you in the future

All the best 👍👍


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A collection of 130 Tools Here:

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Some Useful Top Ai Tools

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20 programming languages that changed their original names👇

1. JavaScript (Originally: Mocha)
2. Python (Originally: Molder)
3. Java (Originally: Oak)
4. C++ (Originally: C with Classes)
5. Ruby (Originally: DLite)
6. PHP (Originally: Personal Home Page Tools)
7. Perl (Originally: Pearl)
8. Rust (Originally: Graydon)
9. Swift (Originally: Bob)
10. Kotlin (Originally: Jet)
11. ECMAScript (Originally: MochaScript)
12. TypeScript (Originally: Script#)
13. Go (Originally: Go Lang)
14. Scala (Originally: Scalable Language)
15. Julia (Originally: Cathy)
16. Haskell (Originally: ISWIM)
17. Lua (Originally: Lua Script)
18. Pascal (Originally: P-System)
19. Visual Basic (Originally: BASIC Interpreter)
20. Delphi (Originally: Turbo Pascal)

Other notable mentions:

C# (Originally: COOL)
F# (Originally: FSharp)
TypeScript (Originally: Script#)
ActionScript (Originally: Flash Script)

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Reasons for name changes:

1. Trademark issues
2. Rebranding
3. Expanded capabilities
4. Avoiding confusion
5. Reflecting language features
6. Merging with other languages
7. Changing language focus
8. Improving marketability

Top 10 programming languages & frameworks for beginner web developers:

1. HTML/CSS – Basics of web structure & styling
2. JavaScript – Adds interactivity
3. Python – Backend & versatility
4. PHP – Server-side scripting
5. SQL – Database management
6. Ruby on Rails – Easy backend framework
7. Node.js – JavaScript backend runtime
8. React – Popular frontend library
9. Angular – Framework for building dynamic UIs
10. Bootstrap – Simplifies responsive design

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Python Tip

WHAT FOR WHAT?
🖼 Frontend
HTML + CSS
Javascript
React
VueJs
Angular
Svelte

🔙 Backend:
Nodejs/Express
Python/Django
PHP/Laravel
Java
C#

💽 Database
MongoDB
MySQL
Postgres
Redis

🖥 Desktop
Electron
Tairi
PyQt

📱 Mobile:
React Native
Flutter
Swift
Kotlin

🖥 System
Go
C++
Rust

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Mastering Java: Roadmap to Complete Mastery

1. Fundamentals
2. Control Structures
3. Object-Oriented Programming (OOP)
4. Advanced Java
| |-- Collections Framework
| | |-- List (ArrayList, LinkedList)
| | |-- Set (HashSet, TreeSet)
| | |-- Map (HashMap, TreeMap)
| | |-- Queue (PriorityQueue, LinkedList)
5. .
6. .
7. .
Detailed Roadmap Here: https://techurl.in/pIsqP

TOP 20 Essential Algorithms

https://x.com/tech_psyche/status/1844360697167348230

Python interview questions 😍😍

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics, automatic memory management.

What's the difference between a tuple and a list?

Both tuples and lists are data structures in Python and hold a list of values. Unlike lists, tuples are immutable - they can't be changed.


What is a dict and what's its most important limitation?

A dict is a structure akin a hash map. It stores key-value pairs, where keys are unique and it has O(1) access time. The most important limitation for a dict is that the keys must be hashable/immutable. Meaning, we can use a tuple as a key, but not a list.


What is pickling/unpickling?

Pickling is converting an object to a string representation in python. Generally used for caching and transferring objects between hosts/processes.

Is Python a Scripting Language?

Python is capable of scripting, but it is more than that. It is considered as a general-purpose programming language.

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𝗚𝗢𝗢𝗚𝗟𝗘 𝗗𝗢𝗥𝗞𝗦 - 𝗢𝗡𝗘 𝗦𝗧𝗢𝗣 📌

•Shodan Search
•API (WSDL)
•GIST github search
•Apache Config Files
•Install/Setup Files
•Apache Struts RCE
•htaccess/phpinfo()
•Security Headers
•Source Code PublicWWW
•Search GitLab and Github
•Find .php (WayBack)
•Find Subdomains (Google)
•Digital Ocean Space
•Sub-Subdomains (Google)

Link 🔗:-

https://techurl.in/cyCjo

Free Labs to Elevate your skills and practice 🎯 #Security

* Academy Hackaflag BR - hackaflag.com.br
- Attack-Defense - attackdefense.com
* Alert to win - alf.nu/alerti
* CTF Komodo Security - ctf.komodosec.com
* CMD Challenge - cmdchallenge.com
* Exploitation Education - exploit.education
* Google CTF-capturetheflag.withgoogle.com
* More Resources - t.me/TechPsyche
* Hack TheBox - hackthebox.com
* Hackthis - hackthis.co.uk
* Hacksplaining - hacksplaining.com/lessons
* Hacker101-ctf.hacker101.com
* Hacker Security - capturetheflag.com.br
* Hacking-Lab - hacking-lab.com
* HSTRIKE - hstrike.com
* ImmersiveLabs - immersivelabs.com
* NewbieContest - newbiecontest.org
* Resources - t.me/TechPsyche
* OverTheWire - overthewire.org
* Practical Pentest Labs - lnkd.in/esq9Yuv5
* Pentestlab - pentesterlab.com
* Penetration Testing Practice Labs - amanhardikar.com/mindmaps/Pract…
* PentestIT LAB - lab.pentestit.ru
* PicoCTF - picoctf.com
* PWNABLE - pwnable.kr/play.php
* Root-Me-root-me.org
* Root in Jail - rootinjail.com

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