Rust Cheat Sheet

https://bit.ly/3YkgUwt

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Google Unveils SynthID: A Tool to Watermark AI-Generated Text for Authenticity

Read Full Update Here:
https://bit.ly/4ePmIVM

SynthID was announced last year August and started as a tool to imprint AI imagery in a way that humans can't visually decipher — but can be detected by the system. The approach is different from other aspiring watermarking protocol standards like C2PA, which adds cryptographic metadata to AI-generated content.

HTML Cheat Sheet

16 Websites to Find Remote International Jobs

1. LinkedIn - linkedin.com
2. Indeed - indeed.com
3. Glassdoor - glassdoor.com
4. FlexJobs - flexjobs.com
5. Remote.co - remote.co
6. Upwork - upwork.com
7. Freelancer - freelancer.com
8. Fiverr - fiverr.com
9. Guru - guru.com
10. Toptal - toptal.com
11. AngelList - angel.co
12. SimplyHired - simplyhired.com
13. Remotive - remotive.com
14. Hired - hired.com
15. CloudPeeps - cloudpeeps.com
16. TaskRabbit - taskrabbit.com

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10 awesome frontend development YouTube channels:

1. Traversy Media 🚀
2. The Net Ninja 🥷
3. Dev Ed 🎨
4. Academind 📚
5. Fireship 🔥
6. Codevolution 💻
7. DesignCourse 🎨
8. Florin Pop 🧑‍💻
9. Web Dev Simplified 🌐
10. Kevin Powell 🎥

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JavaScript Array Methods

What Python Can Do

Road to API Expertise

https://x.com/tech_psyche/status/1847893803807486180

*Here's a good list of cheat sheets for programmers (all free):*

Data Science Cheatsheet

https://github.com/aaronwangy/Data-Science-Cheatsheet

SQL Cheatsheet

sqltutorial.org/sql-cheat-sheet

https://bit.ly/SQL-CheatSheet

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

Java Programming Cheatsheet

https://introcs.cs.princeton.edu/java/11cheatsheet/

Javascript Cheatsheet

quickref.me/javascript.html

bit.ly/JavaScript-Cheatsheet

Data Analytics Cheatsheets

https://bit.ly/40fpy1J

Channel Link
https://t.me/TechPsyche

Python Cheat sheet

quickref.me/python.html

https://bit.ly/3AcFffI

GIT Cheatsheet

https://education.github.com/git-cheat-sheet-education.pdf

https://telegra.ph/Git-Cheat-Sheet-Atlassian-10-23

https://about.gitlab.com/images/press/git-cheat-sheet.pdf

HTML Cheatsheet

https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf

htmlcheatsheet.com

CSS Cheatsheet

htmlcheatsheet.com/css

jQuery Cheatsheet

https://bit.ly/3A9L8dw

Free entry to our WhatsApp channel

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9 Best Machine Learning Use cases in our Daily Lives 🚀

👓 Youtube Recommendation
👓 Voice Assistants
👓 arrow Smartphone Camera
👓 Google Maps routes
👓 Email Filtering
👓 Search
👓 Translation
👓 Chatbots
👓 Fraud Protection

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Machine Learning Cheat Sheet

Here is the list of latest trending tech stacks in 2024 👇👇

1. Frontend Development:
   - React.js: Known for its component-based architecture and strong community support.
   - Vue.js: Valued for its simplicity and flexibility in building user interfaces.
   - Angular: Still widely used, especially in enterprise applications.

2. Backend Development:
   - Node.js: Popular for building scalable and fast network applications using JavaScript.
   - Django: Preferred for its rapid development capabilities and robust security features.
   - Spring Boot: Widely used in Java-based applications for its ease of use and integration capabilities.

3. Mobile Development:
   - Flutter: Known for building natively compiled applications for mobile, web, and desktop from a single codebase.
   - React Native: Continues to be popular for building cross-platform applications with native capabilities.

4. Cloud Computing and DevOps:
   - AWS (Amazon Web Services), Azure, Google Cloud: Leading cloud service providers offering extensive services for computing, storage, and networking.
   - Docker and Kubernetes: Essential for containerization and orchestration of applications in a cloud-native environment.
   - Terraform: Infrastructure as code tool for managing and provisioning cloud infrastructure.

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5. Data Science and Machine Learning:
   - Python: Dominant language for data science and machine learning, with libraries like NumPy, Pandas, and Scikit-learn.
   - TensorFlow and PyTorch: Leading frameworks for building and training machine learning models.
   - Apache Spark: Used for big data processing and analytics.

6. Cybersecurity:
   - SIEM Tools (Security Information and Event Management): Such as Splunk and ELK Stack, crucial for monitoring and managing security incidents.
   - Zero Trust Architecture: A security model that eliminates the idea of trust based on network location.

7. Blockchain and Cryptocurrency:
   - Ethereum: A blockchain platform supporting smart contracts and decentralized applications.
   - Hyperledger Fabric: Framework for developing permissioned, blockchain-based applications.

8. Artificial Intelligence (AI) and Natural Language Processing (NLP):
   - GPT (Generative Pre-trained Transformer) Models: Such as GPT-4, used for various natural language understanding tasks.
   - Computer Vision: Frameworks like OpenCV for image and video processing tasks.

9. Edge Computing and IoT (Internet of Things):
   - Edge Computing: Technologies that bring computation and data storage closer to the location where it is needed.
   - IoT Platforms: Such as AWS IoT, Azure IoT Hub, offering capabilities for managing and securing IoT devices and data.

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Enjoy Learning 🤝🤝

20 Common Lies about Artificial Intelligence

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.

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Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace

7 Websites to Learn Data Science for FREE🧑‍💻

✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp

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Data Science Resources: https://t.me/DataScienceResourcesTP

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Remember, I'm here for you😊I provide anything I think you might need as long as it's in Tech🥳

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Coding Interview Preparation:
Top 10 Sites to review your resume for free:

1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv

Coding is tricky. Coding in interviews feels even harder. It’s intimidating, uncertain and hard to prepare. Here are 4 ways to do it!

1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com

2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/

3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf

4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/

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6 Data Concepts

Fundamental concepts in statistics for entry-level data analysts.

➡️ Descriptive Statistics

👉 Mean: The average value of a dataset.
👉 Median: The middle value of a dataset.
👉 Mode: The most frequently occurring value in a dataset.
👉 Range: The difference between the highest and lowest values.
👉 Variance: Measures how much the values in a dataset vary from the mean.
👉 Standard Deviation: The square root of the variance, representing the average distance of each data point from the mean.

Basics Of HTML

Linux File System in 1 Page

Which Jobs AI Can't Replace?! 😶‍🌫️

https://techurl.in/dIJaG