How to Reach Recruiters on LinkedIn (The Smart Way!) 💼✨

Looking for a job? Want to get noticed by recruiters?

Here’s how you can professionally reach out and actually get a response 👇

✅ 1. Polish Your Profile First
Your profile is your first impression — make it count!
📌 Use a professional photo
📌 Write a clear, concise headline
📌 Highlight your key skills & experience
📌 Add a compelling “About” section

✅ 2. Search for the Right Recruiters
Use keywords like:

🔹 “Recruiter + [Company Name]”
🔹 “Talent Acquisition + [Domain/Role]”
This helps you connect with recruiters relevant to your field.

✅ 3. Send a Personal Connection Request 📨


Don’t just hit Connect — always add a note! Here's a simple format:

“Hi [Name], I admire the work your team is doing at [Company]. I’m exploring opportunities in [role/domain] and would love to connect with you. Thanks in advance!”

✅ 4. Engage Before You Message
Like their posts. Leave thoughtful comments. Show up on their radar before starting a conversation. 👀

✅ 5. Don’t Immediately Ask for a Job ❌

Build a connection first. Instead, ask: 💬 “Could you share what qualities you usually look for in [role] candidates?”
This shows initiative without pressure.

✅ 6. Follow Up Respectfully 🔁

If you don’t get a response, follow up once after a few days.
Keep it short, friendly, and respectful. Patience pays.

✅ 7. Be Active & Consistent 📣

Post updates, share your learnings, and interact with industry content.
Recruiters often notice candidates who are visible and engaged!

💡 Remember: LinkedIn is not just about job hunting – it's about building professional relationships.


Stay genuine. Stay professional. Stay consistent. 🌟

✨ You never know which message could change your career path.

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❎ High-Resolution VITON-Zalando Dataset.

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📝 Ghibli Dataset

❓Original to #Ghibli art image generator and classifier dataset.

The Ghibli Art Image Dataset is a prototype designed for generating Ghibli-style images using machine learning. It includes three subsets—training, testing, and validation—each containing directories with two PNG images: one original (o.png) and one generated in Ghibli style (g.png). This dataset supports tasks like image classification and Ghibli-style image generation. As a small-scale version of a larger dataset, it provides essential resources like model code and a pre-trained Generator.pth model. The images were collected from platforms such as Meta, Google, and Instagram for research and experimentation purposes.

Ghibli Dataset

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Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview

1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.

2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.

3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.

4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.

5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.

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