Learning Machine Learning from Scratch with NumPy and Next.js

This week I didn't just write code. I understood what's happening inside it. Here's a recursive function I built 👇 It calculates hotel earnings for N days at ₹5000/day — no loops allowed, only recursion. Watch how Python actually executes it step by step 🎥 What I covered this week: → 🔁 Recursion — functions calling themselves → 📁 File I/O — reading & writing files with Python → 🌐 Internet fundamentals — TCP/IP, HTTP, DNS, REST APIs (Phase 4 done!) Still learning. Still showing up. That's the only formula I know. 💪 💻 Visualization tool: staying.fun 🐍 Language: Python #Python #LearningInPublic #DataAnalytics #Recursion #BBA #100DaysOfCode

Stop fixing, start scaling. 🚀 We’ve all been there: you build a scraper, it works perfectly, and then—one small website update later—your entire pipeline is broken. It’s a frustrating cycle that holds your data back. It’s time to move away from fragile, "quick-fix" scripts and toward enterprise-grade data infrastructure. We’ve put together a complete guide to help you master web scraping with Python and build systems that actually last. Check out the full guide here: https://lnkd.in/g-NQk3SJ #WebScraping #Python #DataEngineering #BigData #Boundev

🚀 Learn Python in 30 Days (Simple Plan) Week 1: Basics 👉 Variables, data types, if-else, loops Week 2: Core Concepts 👉 Lists, dictionaries, functions, file handling Week 3: Intermediate 👉 OOP, modules, error handling + practice problems Week 4: Real Skills (choose one) 💻 Web (Flask) 📊 Data Science (Pandas, NumPy) 🤖 Automation (scripts, bots) Daily Routine (1–2 hrs): ✔ Learn → Practice → Build 💡 Tip: Don’t just watch tutorials — code every day. #Python #Coding #LearnToCode #Developer

The best debugging session is a build. Recently put together a small project using Python and Pandas. What it forced me to think through: 🔹 Structuring DataFrames for the right operations — not just loading data, but designing it 🔹 Vectorized operations over loops — cleaner, faster, more Pythonic 🔹 Filtering, grouping, and aggregating with intent 🔹 Serialization — reading from and writing back to files mid-workflow 🔹 Knowing when Pandas is the right tool and when it's overkill That last point is underrated. A DataFrame isn't always the answer. Knowing when a plain Python dict does the job better — that's the actual skill. Real constraints teach what tutorials skip. #Python #Pandas #DataEngineering #BuildingInPublic #SoftwareCraft #DataScience

Hey everyone! 👋 Ever wondered what this 'NumPy' thing is all about? I'm sharing some basics! Here’s the simple breakdown for everyone! ✅ 1. Getting Ready First, you gotta download the tool, like an app! ⬇️ Type this: pip install numpy ✅ 2. Inviting It Over Then you tell Python to use it. Think of it like inviting NumPy to a party. We even give it a short nickname, like 'np', so it's easier to talk to! 🗣️ Type this: import numpy as np. ✅ 3. Making a Row of Numbers This is the fun part! You can make a long line of numbers, just like a train with different cars. 🚂 We call this a "1D array." Type this: arr1 = np.array([1, 2, 3, 4, 5]). ✅ 4. Checking Our Creation Then you can see what kind of thing you made and what's inside it. 🧐 Check type: type(arr1) (it’ll say it's a special NumPy thing!) Look inside: arr1 This is super simple but incredibly powerful for handling numbers in Python. Check out the short cheat sheet below for a quick reminder! 👇 Short Cheat Sheet for Newbies | Action | Code | What it Does | | Download | pip install numpy | Gets the NumPy tool. | | Use It | import numpy as np | Lets Python use NumPy with nickname 'np'. | | Make Numbers | np.array([1,2,3]) | Creates a simple row of numbers. | What are you guys using NumPy for these days? Let me know! #python #datascience #numpy #coding #learning #beginnerfriendly

Ever wondered how agents actually work under the hood? I did. So I built it from scratch. tinyagents.dev — a free, browser-based course that teaches AI agents from first principles. No frameworks. No abstractions. Just Python. 9 lessons. Each adds one concept. By lesson 9, you've built a complete agent in 60 lines of code. Two modes: → Mock mode — works instantly, no keys needed → Live mode — plug in a free Groq API key and talk to a real LLM No installs. No signup. Runs entirely in your browser. Open source. Try it here : https://tinyagents.dev #AIAgents #Python #OpenSource #LLM

AI agents are the new buzzword — but very few resources explain how they actually work under the hood. My friend Arun P. built tinyagents.dev, a super clean browser-based course that walks through building an AI agent from first principles. No frameworks. No magic. Just Python. In 9 short lessons, you go from zero → a working agent (~60 lines of code). It even has: • Mock mode (no API key needed) • Live mode (connect to Groq for real LLM interaction) • Runs fully in the browser Worth checking out if you're exploring AI agents, LLM tooling, or building your own assistants. https://tinyagents.dev

🚀 Day 1: The Foundation & The "Aha!" Moment Focus: Variables & Fundamentals Today, I officially wrote my first lines of Python after a long time, and let’s just say… it was a beautifully humbling experience. 😅 I realized that even the "simplest" tasks—like adding numbers or printing a sentence—require total precision. Python is gentle until you forget a single quote mark, and then it’s game over! What I tackled today: The Execution Flow: Understanding how Python reads code from top to bottom. The Power of Variables: They’re essentially just containers, but naming them is an art form. The Rules: Learned why (my_var) works, but (2_var) breaks everything. Data Types: Realizing that 10 (integer) and "10" (string) might look the same to us, but they are worlds apart to a computer. It’s exciting, a little frustrating, and 100% worth it. 😁 #Python #Day1 #LearningToCode #TechBeginner #CodingFromScratch

🚀 Streamlit Full Course: From Beginner to Advanced Want to master Streamlit and build interactive data apps with ease? I've put together a comprehensive, free course covering everything from basic widgets to advanced dashboard techniques. 📚 Explore the full course: https://lnkd.in/gTSBpuvi 🔹 What you will learn • Streamlit Installation & Setup • Core display methods (st.write, st.title, st.header) • Interactive widgets (text_input, slider, checkbox, toggle) • Data visualization with charts and tables • Layouts, sidebars, and multi-page apps • Real-world project examples Happy Learning! 🚀 #Streamlit #Python #DataScience #WebDevelopment #DataApps #MachineLearning

📣 SynapseKit v0.6.8 is live. Your agents can now search PubMed, GitHub, and YouTube. Send emails. Query your own vector store. All with zero new dependencies for most of it. That last one matters more than it sounds- every tool you add to an agent is a potential point of failure. We built these to be stdlib-first wherever possible. Also in this release: WebSocket streaming for graph workflows and structured execution tracing with timestamps. So when something breaks in production, you know exactly where and how long each node took. What SynapseKit looks like today: ⚡ 743 tests 🔌 15 LLM providers 🛠️ 29 built-in tools 🔍 18 retrieval strategies 🧠 8 memory backends 📄 14 document loaders 💾 4 cache backends 🔗 2 hard dependencies Async-native from day one. Not retrofitted. No hidden chains. No magic. Just Python you can actually read. pip install synapsekit 🔗 https://lnkd.in/d2fGSPkX #Python #LLM #RAG #OpenSource #AI #MachineLearning #Agents #SynapseKit