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leveraging referrals

Leveraging Referrals and LinkedIn Efficiently for Off-Campus Placements (2025 Guide)

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Getting a job after graduation has never been a simple journey. For most freshers in India, on-campus placements cover only a small percentage of opportunities. If you miss that train or if your college doesn’t attract big recruiters, it can feel like you’re left behind. But here’s the good news: off-campus placements are becoming more powerful than ever—and the two tools that can give you a real edge are referrals and LinkedIn. Whether you are a 2025 graduate just starting your journey, or someone still struggling after months of sending applications, this guide will help you understand how to make these tools work for you. Why Referrals Are the Secret Weapon for Freshers Imagine two candidates applying for the same role at Infosys or TCS. One simply applies through the company’s career page. The other gets an internal referral from someone working in the company. Guess who gets noticed first? Recruiters handle hundreds, sometimes thousands, of resumes daily. A referral doesn’t guarantee selection, but it ensures your resume gets reviewed seriously. LinkedIn research shows that referred candidates are 4x more likely to get shortlisted than those applying directly. For freshers, especially from Tier 2 or Tier 3 colleges, referrals can act as an equalizer. They don’t care about your college brand—only about whether an employee trusts you enough to refer. How to Ask for a Referral (Without Being Awkward) Most students shy away because they feel like they’re “bothering” others. But the truth is, professionals receive referral requests often, and many are willing to help if you approach them right. The key is politeness and personalization. Steps to Request a Referral Do your research first: Find employees on LinkedIn who work in the company and department you’re targeting. Prioritize alumni from your college or region—they’re more likely to help. Send a connection request with context: Example: “Hi [Name], I’m a final-year Computer Science student from [College]. I admire your journey at [Company]. I’d love to connect and learn from your experience.” Once accepted, send a polite referral message: Example: “Thank you for connecting! I’m very interested in applying for the [Role] at [Company]. If you’re comfortable, could you kindly refer me? I’ll share my resume and job ID for convenience.” Golden Rule: Never send your resume in the very first message. Build rapport, even if briefly. LinkedIn: More Than Just a Job Portal Most freshers treat LinkedIn like a place to dump their resume and forget. But LinkedIn is not just a digital CV—it’s a professional network where recruiters, hiring managers, and employees actively search for talent. If your profile is optimized, you don’t always have to chase recruiters. Sometimes, they’ll find you. Step 1: Optimize Your LinkedIn Profile Profile Photo: Use a clear, professional-looking headshot. Avoid selfies or casual group pics. Headline: Instead of just “B.Tech Student,” use something keyword-rich like: “Computer Science Graduate | Skilled in Java, React, SQL | Aspiring Software Engineer”. About Section: Write a short story. Mention your passion, key skills, and career goals in 4–5 sentences. Experience & Projects: Add internships, hackathons, personal projects, and GitHub links. Skills & Endorsements: Highlight in-demand skills (DSA, Python, Cloud, Machine Learning). Recommendations: Request professors, mentors, or internship guides for 2–3 line recommendations. Step 2: Network Without Being Spammy Engage with posts meaningfully—avoid one-word comments. Share your learning journey and coding achievements. Join groups focused on fresher hiring, coding challenges, and off-campus jobs. Step 3: Use LinkedIn Job Search Smartly Apply filters for Location, Experience, and Company. Use Easy Apply for speed, but combine with referrals for better chances. Save jobs to train LinkedIn’s algorithm for relevant openings. Balancing Quantity and Quality in Applications Many freshers apply to 300 jobs with the same resume—this rarely works. Instead: Apply to 10–15 jobs per week. Tailor your resume slightly to each job description. Track applications in a Google Sheet with job details, referral requests, and responses. Real Success Story One of our community members, Priya (B.Tech 2023 graduate from a Tier-3 college), struggled for six months with no callbacks. She then optimized her LinkedIn profile, started sharing weekly posts about her coding projects, and politely reached out to alumni for referrals. Within 45 days, she landed interviews at Cognizant, Capgemini, and HCL—and eventually got two offers. Her exact words: “It wasn’t luck—it was consistency and using LinkedIn smartly.” Final Words: Play the Long Game Landing an off-campus job is not about quick hacks. It’s about showing up consistently—building a visible profile, reaching out politely, and applying strategically. Remember: Referrals open doors faster. LinkedIn helps you build a personal brand. Consistency beats desperation every time. So, instead of refreshing Naukri or Indeed all day, invest an hour daily in LinkedIn networking and referral requests. In 2025, that’s what will set you apart. Key Takeaways Referrals increase your chances by 4x—don’t ignore them. Optimize your LinkedIn profile with keywords, projects, and recommendations. Network genuinely—don’t spam connection requests. Apply smartly: quality + consistency matter more than quantity alone. Share your journey; recruiters love authenticity.

August 30, 2025 / 0 Comments
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Artificial Intelligence concepts

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What is Artificial intelligence?Intelligence is the human ability to implement knowledge and skill as per the required circumstances. In technological term intelligence frequently refer to the ability of a system to use available information, learn from it, make decisions and adapt to new situations. Whereas the term “artificial” means that the intelligence is not inherent but created through design and programming.The concept of “artificial intelligence “is refer to ability of computer system to perform complex activities which is only done my human beings. such as interpretation of data, language understanding, learning from past, detecting trends, demand forecasting, analyzing the gap ,formulation of strategy and its evaluation, managing statistical data etc.But here’s the thing—AI isn’t magic. It’s built on algorithms that improve over time, kind of like how we learn from experience. The more data it gets, the smarter it becomes. Still, it’s not flawless. Ever had autocorrect butcher your text? Yeah, that’s AI missing the mark. It needs good data and human oversight to work well. The real goal of AI isn’t to replace us but to handle the tedious, data-heavy tasks so we can focus on creativity, strategy, and the stuff that actually requires a human touch. Whether it’s healthcare, finance, or even art, AI is just another tool—powerful, but still one that needs us to steer it right. DIFFERENCE BETWEEN HUMAN INTELLEGIENCE AND ARTIFICIAL INTELLIGENCE HUMAN INTELLEGIENCE ARTIFICIAL INTELLIGENCE Non predictable Predictable Human incorporate ethical and moral consideration. Machines cannot understand ethics and morality. Excel in creativity and innovation. Totally depend on available information. Humans solve problem using abstract thinking and intuition. Machines rely on algorithms and pre-defined logic. How AI born?Model of artificial neurons, which is considered as the first artificial intelligence, even though the term was not exist was propounded by McCulloch and Walter Pitts in year in 1943. Later in 1950, an article with entitled “computing machinery and intelligence” was published by British mathematician Alan Turing where he asked question: can machine think?However, he proposed an experiment which is popularly known as Turing test. As per the Alan this test would make it possible to determine whether machine could have intelligent behavior similar to or indistinguishable from that of a human being.John McCarthy coined the term “artificial intelligence” in 1956 and drove the development of the first AI programming language, LISP, in the 1960s. EVOLUTION OF AI: A Timeline Early foundations(1940s-1950s) Model of artificial neurons, which is considered as the first artificial intelligence was propounded by McCulloch and Walter pitts in year in 1943. In 1950, a british mathematician Alan Turing proposed an experiment which is popularly known as Turing test. Dartmouth conference officially coin the terms Artificial intelligence in 1956. Early AI & Symbolic AI(1950s-1970s) In 1956 The first AI programme was developed by Allen Newell, Herbert A. Simon and Cliff which is popularly known as The logical theorist. In 1966 The very first Ai powered Chatbot Eliza was developed . 1970 : AI Winter refers to a period where progress in AI was slowed. Machine learning and data driven approaches (1980s-1990s) Era of major shift: Machine learning now allowed computer to learn patterns from data autonomously. Machine learning helps to improve the performance of machines based on specific tasks. It is used in different applications, such as image recognition and language processing. Rise of modern AI (2000s-2018) Big data & computing power Machine learning goes mainstream Deep learning revolution Natural languages processing (NLP) Advances In Present Generative AI in new trends Generative AI systems create new content – such as text, images, code and even video from vast datasets. Unlike traditional AI (which classifies or predicts), generative models produce original outputs, enabling breakthroughs in creativity, automation, and human-AI collaboration.

May 10, 2025 / 0 Comments
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