Sachin Mittal

Most colleges are still “waiting” on AI agents. Sreenidhi’s SDC SNIST community chose to lead. Honoured to be invited as a guest to run a hands-on workshop on n8n Automations at Sreenidhi College. This is the first college I’ve seen step up and initiate a dedicated agents + automations workshop for students—and the response was incredible. Students were deeply engaged, asked sharp questions, and jumped straight into building. “Understood the basics of n8n and automations quickly—and built a few workflows during the workshop.” What we focused on: - How agentic workflows map cleanly in n8n (from trigger to deployment) - Turning repetitive tasks into hands-free automations - Clean, minimal stacks students can maintain without big budgets I’m grateful to the SDC community for the initiative and to every student who showed up to learn, experiment, and ship. It was a wonderful experience interacting with the Sreenidhi students—you made it special. Special thanks to Chandrashekhar M (Founder, SDC Community) for championing this initiative. — Master n8n automations — If you want to go from zero to deployed fast, join my Agents Master Course (n8n + real-world agent workflows). Practical, project-based, and built for outcomes: https://lnkd.in/gy_Ek-WD Prefer a quick strategy chat to automate your business tasks? Book a 30-min slot: https://lnkd.in/gj597xVS #n8n #Automation #AIAgents #NoCode #SDCCommunity #SNIST #FirstMover #EducationInnovation #StudentDevelopers #HyderabadTech #IndiaTech #Workshops #Repeatless #Btech #Colleges

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I AM REALLY SORRY!! Recently, I was invited to my college to guide students on SIH 2025, but due to work hours on weekdays I couldn’t attend. So, here’s my small effort to make up for it—a post that I hope will give you the same guidance and maybe even push you closer to winning. When our team won Smart India Hackathon, I realized something important: Most teams don’t lose because of weak coding skills. They lose because they don’t know the strategy. Here’s how you can set yourself apart and give your team the best shot at winning: 1. Pick your PS wisely Don’t just pick the trending or obvious “hot" problem statements everyone is rushing for. Find something uncommon—less competition, higher chances. Believe me, this one decision can change your journey. 2. Talk to the people who matter If your problem statement belongs to a ministry/department, connect with people from there on LinkedIn. Ask how they currently handle the problem and what kind of solutions they expect. That insight is gold. Those conversations give you clarity that 90% of teams never have. 3. PPT > Solution (initially) At the start, your PPT decides your fate—not the prototype. - Follow the given structure properly - Sprinkle in the right keywords/phrases so reviewers immediately connect - Don’t rush into building the entire solution before shortlisting—make the PPT your strongest weapon first - Bonus tip: Add a simple UI mockup—it shows intent and planning 4. Double your chances Last year, SIH allowed 2 submissions per team. That means you instantly boost your odds of getting in by 50% if you apply for 2 problem statements. If you follow this, you’re not just participating, you’re strategically playing the SIH game. And trust me, once you’re inside and shortlisted, the real fun begins—the late nights, the brainstorming, the pitching—it’s an unforgettable ride. To every student reading this: You can win SIH 2025. Start today. Build your team, choose wisely, and play smart. Best of Luck to all the participants!! #SIH2025 #SIH #Engineering

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"Learn AI in 30 days" ads are ruining genuine learners. Here's the reality check you need. Back in college, I was fascinated with Machine Learning. No fancy courses, no structured roadmaps. Just curiosity and whatever resources I could find. Built basic projects - Path follower, prediction models. Nothing groundbreaking, but it taught me fundamentals. Fast forward to today: AI has exploded everywhere. But so has the noise. Every YouTube ad promises: "Become AI engineer in 30 days!" "Master Data Science this weekend!" This Results in Complete confusion. You follow 10 different "gurus." Each says something different. You end up more lost than when you started. Here’s the real problem: → No clarity on what actually matters → No focus on what companies need → Too many distractions disguised as "opportunities" Been there. Wished someone had given me clear direction earlier. That's why when I saw Pulkit Aneja (ex-Amazon Data Scientist) doing a free Scaler masterclass on "Transition from Non-Data Science to Data Science Roles," it caught my attention. They’re doing this on 17th July, 7:30 PM Your consistency starts here - https://bit.ly/4luiPsT No course selling. Just practical guidance for developers wanting to enter this space. Sometimes one honest session beats months of YouTube rabbit holes. Also, what's your biggest confusion about entering AI/ML space?

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For many beginners, 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 is the first stepping stone into the world of 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴. But truly mastering ML requires more than just understanding the gradient function—it demands a strong conceptual foundation and a clear grasp of the interconnected topics that underpin it. To help you build that foundation, here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗹𝗲𝗰𝘁𝘂𝗿𝗲 𝘀𝗲𝗿𝗶𝗲𝘀; delivered in Hindi, explained on a whiteboard, and designed to feel like a university classroom experience. Each session runs 45–60 minutes, providing a deep, step-by-step understanding of key ML concepts. 𝗖𝗼𝗿𝗲 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 & 𝗕𝗲𝘆𝗼𝗻𝗱:  • Quartile & Box Plot – https://lnkd.in/d_Qh_q55  • Loss Function & Gradient Descent – https://lnkd.in/dRJP7izX  • Linear Regression & R² Score – https://lnkd.in/dJVf8XNs  • Assumptions of Linear Regression – https://lnkd.in/dDu9gUaQ  • Multicollinearity & VIF – https://lnkd.in/dK7Y2xGf  • Polynomial Regression – https://lnkd.in/geektYCd  • L1 & L2 Regularization – https://lnkd.in/dFi_FH2W  • Hyperparameter Tuning – https://lnkd.in/dhXm-n35  • K-Fold Cross Validation – https://lnkd.in/drC93mmQ  • Encoding Categorical Variables – https://lnkd.in/dpiihcjG  • Interview Preparation – https://lnkd.in/d6n_258W  • End-to-End ML Project – https://lnkd.in/dhpVacVN 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 (𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻):  • Introduction to Logistic Regression – https://lnkd.in/dfarzwkG  • Loss Function & Sigmoid Function – https://lnkd.in/dT-vSvqs  • Gradient Descent Derivation – https://lnkd.in/dH8cc-Du 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗿𝗼𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺:  • The First Classification Algorithm – https://lnkd.in/dVkQqVYe Whether you’re just starting out or preparing for interviews, this series by Pritam Kudale offers practical, deep, and structured learning, helping you strengthen your ML fundamentals the right way.

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