Coding Projects To Get Into Big Tech Companies

Coding Projects To Get Into Big Tech Companies:-

🚀 𝗖𝗼𝗱𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗖𝗮𝗻 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗜𝗻𝘁𝗼 𝗕𝗶𝗴 𝗧𝗲𝗰𝗵 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 💻🔥

Want to land a job at top tech companies like Google, Amazon, Microsoft, Meta, and other leading product-based companies? 🌟

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦- 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬

Building real-world projects is one of the fastest ways to stand out from thousands of applicants and showcase your practical skills. Recruiters and hiring managers value strong project portfolios alongside coding and problem-solving abilities.

📌 Projects That Make Your Resume Stand Out:
✅ Full Stack Web Applications
✅ AI & Machine Learning Projects
✅ Data Analytics Dashboards
✅ System Design & Scalable Applications
✅ Cloud & DevOps Projects
✅ Mobile App Development Projects
✅ Open Source Contributions

🎯 Why Build Projects?
✔️ Demonstrate Real-World Problem Solving
✔️ Strengthen Your Resume & GitHub Profile
✔️ Gain Practical Development Experience
✔️ Prepare for Technical Interviews
✔️ Stand Out During Campus & Off-Campus Hiring

💡 Top tech companies look for candidates who can apply their knowledge to solve real business problems, not just clear coding rounds. Strong projects can significantly improve your chances of getting interview calls.

👨‍💻 Perfect For:
🎓 Students
💼 Freshers
🚀 Aspiring Software Engineers
📊 Data Analysts
🤖 AI/ML Enthusiasts

⏳ Start Building Today. Your Dream Tech Job Could Be One Project Away!

Want to land a job at a big tech companies? 🚀

Showcase the right coding projects! 💻

From algorithm-based solutions to real-world applications, companies like Google, Amazon, and Microsoft value practical, scalable projects. Focus on areas like data structures, machine learning, and cloud computing to stand out.

Start building now!

Portfolio Website(Week 1):

Description: Develop a personal portfolio
website to showcase your projects, skills,
and experiences.
Technologies: HTML, CSS.
Details:
Create a visually appealing and responsive
design.
Include sections for your biography, skills,
project showcases, resume download link,
and contact information.
Implement interactive elements such as
animations and hover effects.
Ensure the website is optimized for
performance and accessibility.

Pantry Tracker(Week 2):

Description: Build an application to
manage and track pantry items, ensuring you
are aware of stock levels and expiry dates.
Technologies: React, Next.js, Material- UI
(MUI), Firebase.
Details:
Develop a user-friendly interface to add,
update, and delete pantry items.
Integrate a Firebase backend for real-time
data storage and synchronization.
Implement features such as notifications
for low-stock items and nearing expiration
dates.
Utilize Material-Ul for styling and enhancing
user experience.

Customer Support AI (Week 3):

Description: Create an AI-powered
customer support system to assist users
with common queries and issues.
Technologies: Next.js, OpenAI, AWS EC2,
Lambda.
Details:
Develop a chatbot using OpenAl’s language
model to handle customer inquiries.
Deploy the application on AWS using EC2
instances and Lambda functions for
scalability and reliability.
Implement conversational flows and train
the Al to provide accurate and helpful
responses.
Include a fallback mechanism to transfer
complex queries to human agents

Flashcard with Paywall(SaaS) (Week 4):

Description: Develop a SaaS application
for creating and managing flashcards with
a paywall feature for premium content.
Technologies: OpenAl, Authentication,
Payment Gateway, Server-side
programming.
Details:
Create a system for users to generate
and organize flashcards for studying.
Implement authentication and user
management to ensure secure access.
Integrate a payment gateway to allow
users to purchase premium flashcard sets
or subscription plans.

Rate My Professor RAG (Week 5):

Description: Build a review aggregation
platform where students can rate and review
their professors.
Technologies: Embeddings, Vector Databases.
Details:
Develop a frontend interface for users to
submit and view reviews of professors.
Use embeddings to generate meaningful
representations of reviews for efficient
searching and indexing.
Implement a vector database to store and
retrieve reviews based on similarity and
relevance.
Include features such as filtering by
department, rating criteria, and sortina by most helpful reviews.

For More Details:– https://drive.google.com/file/d/1B2bZFrBBj9DWTVKE7m_7k5mMkcYwDsl8/view

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