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Day 60/60 ✅ I kept my promise. 60 days ago, I started with curiosity. Today, I finish with projects, confidence, and clarity. This journey taught me one thing: 👉 You don’t need perfection — you need
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Downloaded or cloned a Machine Learning project from GitHub but don’t know how to run it? 🤔 In Day 59, I explain the exact steps to run any ML project locally — from cloning the repo, installing depe
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Day 58 – How to Download or Clone a GitHub Repo 🚀 Confused between Download ZIP and Git Clone? This reel explains both methods in a simple way so beginners can easily use GitHub projects. Learn how t
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Day 57/60 🚀 What is Git and GitHub? 🤔 Every developer uses Git to track code and GitHub to collaborate, share, and build real-world projects. If you want to grow in ML, Web, or Software Development,
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Day 56/60 – How to Explain Your Project Clearly (For Interviews, GitHub & Demos) Explaining your project is just as important as building it. On Day 56, I’m sharing a simple and structured way to expl
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From idea to deployment 🚀 An end-to-end ML project covers the full journey—problem understanding, data collection, preprocessing, feature engineering, model training, evaluation, deployment, and moni
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Algorithm selection isn’t about using the fanciest model ⚠️ It’s about data, constraints, and goals. In Day 54, I explain how ML engineers choose the right algorithm based on problem type, data size,
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ML in real life ≠ just training a model ❌ It’s a full system working 24/7 ⚙️ From data → model → API → monitoring → retraining This is how ML powers Netflix, Google, UPI fraud detection & more 🚀 Day
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Low accuracy isn’t the end ❌ It’s a signal to improve 🔁 Day 52: Practical methods to boost ML model performance 🚀 ✔ Better data ✔ Smarter features ✔ Right tuning ✔ Correct evaluation Build models li
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🚫 Beginner Mistakes in Machine Learning Most ML beginners fail not because ML is hard, but because they repeat the same mistakes 👀 ❌ Ignoring data ❌ Overfitting models ❌ Trusting accuracy blindly ❌
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Day 50: How ML Projects Are Structured 🧠📂 Most beginners write ML code in a single file 😅 But real-world ML projects follow a clean and modular structure. In Day 50, you’ll understand: 📁 How ML pr
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Day 49/100 – Saving & Loading ML Models 💾🤖 Training a model is not enough ❌ Learning how to save and reuse it is what makes your ML project production-ready 🚀 Today I learned: ✔ Why model saving ma
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About @ns_algohub

NICK is a Instagram growing creator in the Digital creator category. With 33 followers and 12 posts, ns_algohub's account shows a follower-to-following ratio of N/A:1. View all content anonymously on Pictame - no login required.

ns_algohub's engagement rate sits at 36.62%. That's 632% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. New content goes up pretty much every day — a very consistent posting habit. Captions lean moderate, averaging 491 characters.

36.62% engagement rate — 632% above average for 1K-10K follower accounts.
Posts every day — very consistent.

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ns_algohub
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@ns_algohub

NICK

PublicDigital creator
Posts
12
Followers
33
Top Hashtags
#MLJourney#MachineLearning#DataScienceLife#MachineLearningJourney#LearnMachineLearning#GitHub#100DaysOfML#Day60

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

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Popular Hashtags

#photographer#sun#trend#workout#instafood#baby#follow4follow#amor#interiordesign#illustration#home#tiktok
Following
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Avg. Likes
12
Avg. Comments
0
Engagement
36.36%

Python • ML • Projects Learning in public Learn • Code • Improve Target → 100 followers YouTube → @NSAlgoHub

youtube.com/@nsalgohub?si=0HKJ...
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