Linux concepts you should learn for DevOps 1. Accessing Linux VMs 2. Linux File System 3. Soft and Hard links 4. Files & Directory permissions(chmod) 5. ACL 6. Text processing commands(awk, sed, cut, grep, sort, uniq, wc) 7. Compressing files: tar, zip/unzip 8. Vi, nano editor 9. User account management 10. Commands for administration: top, ps, kill, df, dmesg.

Small progress every day beats motivation once a month. 🚀 Keep going.

Want to become job-ready in tech? Stop jumping from one tutorial to another. Step-1: Learn the Basics Step-2: Understand How Things Work Step-3: Practice Daily Step-4: Build Mini Projects Step-5: Learn Industry Tools Step-6: Read Official Documentation Step-7: Break Things and Fix Them Step-8: Automate Repetitive Tasks Step-9: Create a Portfolio Step-10: Share Your Work Publicly Step-11: Prepare for Interviews Step-12: Never Stop Learning

🐍 5 Python Modules Beginners Should Know 1. math For mathematical operations. 2. random Generate random numbers and choices. 3. datetime Work with dates and time. 4. os Interact with the operating system and files. 5. json Work with JSON data. Python comes with powerful built-in modules. Learn to use them. Save this for your Python journey! 🔖🚀

Shit internet is broken after seeing 4 best course and resources 1. Learn Docker docker.com/trainings/ 2. Linux Unhatched netacad.com/courses/linux-un… 3. GitLab University university.gitlab.com/ 4. DevOps Engineer. skills.google/paths/20 Save to unlimited free resources to you And kept follow

• ☁️ Cloud Projects → Showcase infrastructure skills • 🐳 Docker Projects → Demonstrate container knowledge • ☸️ Kubernetes Manifests → Show orchestration skills • 📚 Documentation → Prove communication skills A GitHub profile is more than a code repository. It’s a portfolio of your learning journey. Don’t wait until you’re “good enough” to start. Start building. Start documenting. Start sharing. Your future opportunities may come from a repository you create today.

If you’re learning tech, don’t just collect certificates. Build a GitHub profile. 🚀 Your GitHub can show employers what your resume can’t. Here’s what to put on it: • 📂 Personal Projects → Show what you’ve built • 📝 README Files → Explain your work clearly • 🔀 Git Commits → Demonstrate consistency • 🐛 Bug Fixes → Show problem-solving skills • 🤝 Open Source Contributions → Collaborate with others • ⚡ Automation Scripts → Solve real-world problems

If I were learning Python from ZERO, these are the 10 projects I'd build: 1. Calculator 2. Number guessing game 3. Password generator 4. Quiz app 5. To-do list 6. Expense tracker 7. Web scraper 8. Weather app 9. Automation script 10. Full application The key: Don't build all 10 at the same difficulty. Each project should force you to learn something new.

10 AI PLATFORMS TO START LEARNING HTML, JAVA, CSS, AND PYTHON 1. SkillBuilder 2. Google Skills 3. Udemy.com 4. edX.org 5. Codewars.com 6. Codecademy 7. SoloLearn 8. CoderZap.com 9. Freecodecamp.org 10. Replit

Step-12: Keep Learning DevOps Best Practices 🏆 Congratulations, you’re on your way to becoming a DevOps Engineer!

Don’t overthink it….just start. Step-1: Build Strong Linux Fundamentals Step-2: Learn Git and Version Control Step-3: Understand Networking Basics Step-4: Practice Bash and Python Scripting Step-5: Learn CI/CD (Jenkins, GitLab) Step-6: Get Hands-On with Docker Step-7: Master Kubernetes Step-8: Learn Cloud (AWS, Azure, GCP) Step-9: Understand IaC (Terraform, Ansible) Step-10: Learn Monitoring & Logging (Prometheus, ELK) Step-11: Automate as Much as Possible!

10 Excellent and Powerful Platforms for Learning HTML, Java, CSS, and Python 1. Scrimba.com 2. GitHub 3. Coursera 4. Datacamp.com 5. Khanacademy.org 6. W3Schools 7. Mimo 8. The Odin Project 9. Hackerrank.com 10. Leetcode.com

Free Courses With Certificates By Google 👇 1/ AI Essentials & ML: grow.google/intl/en_in/ai-es… 2/ Cybersecurity: grow.google/intl/en_in/cyber… 3/ Project Management: grow.google/intl/en_in/proje… 4/ Data Analytics: grow.google/intl/en_in/data-… 5/ Advanced Data Analytics: grow.google/intl/en_in/data-… 6/ UX Design: grow.google/intl/en_in/ux-de… 7/ IT Automation with Python: grow.google/intl/en_in/it-su…

6 stages to learn SQL properly : • Stage 1: SELECT, FROM, WHERE • Stage 2: ORDER BY, LIMIT • Stage 3: GROUP BY, HAVING • Stage 4: JOINs • Stage 5: Subqueries + CTEs • Stage 6: Window Functions

15. Serrano Academy — simplified ML/DL concepts 16. Tina Huang — learning systems + AI career advice 17. Matt Wolfe — AI tools and news in under 10 minutes 18. AI Explained — no-hype breakdowns 19. The AI Advantage — AI for work, productivity, automation 20. Hamel Husain — LLMs, RAG, fine-tuning Learn a little every day. Small steps today. Big impact tomorrow.

8. ML Street Talk — sharp discussions on where AI is going 9. freeCodeCamp — free coding + AI certs 10. Sentdex — Python, ML, and projects step by step 11. Data School — data analysis + ML the practical way 12. Codebasics — real-world ML for jobs 13. Siraj Raval — story-driven tutorials 14. Google Cloud Tech — deploy models like a pro

20 YouTube channels that make learning AI actually doable. Save this. Bookmark it. Come back when you get stuck. 1. 3Blue1Brown — the math, made visual 2. Andrej Karpathy — beginner-friendly deep lectures 3. Lex Fridman — conversations with the people building it 4. StatQuest — ML + stats without the pain 5. Jeremy Howard — practical deep learning with Python 6. Two Minute Papers — research in 2 minutes 7. DeepLearning AI — structured courses from Andrew Ng

4 PROJECTS. 4 SKILLS. Build these instead of 20 random projects: 01 — SALES Excel → Reporting + KPIs 02 — CUSTOMER SQL → Segmentation + behavior 03 — FINANCE Power BI → Dashboard + trends 04 — MARKETING Python → EDA + insights For every project, show: Problem Analysis Insight Recommendation Your portfolio should tell recruiters: "I don't just use tools. I solve problems."

📚 Books I’m following for my AI/ML journey: 1. AI Engineering — Chip Huyen 2. Hands-On Machine Learning — Aurélien Géron 3. Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst 4. Build a Large Language Model (From Scratch) — Sebastian Raschka Building my foundation in ML → DL → LLMs → AI Engineering. 🚀

If I had to learn Computer Science from ZERO in 2026: 1. Computer fundamentals 2. Python 3. Git & GitHub 4. Data structures 5. Algorithms 6. Databases 7. Operating systems 8. Computer networks 9. APIs 10. Real projects The biggest mistake? Trying to learn everything at once. Follow ONE path. Build as you learn.