AI Coding Speed Matters: Vanishing Gradients with Hugo Bowne-Anderson

Developed a simple Linear Regression model to predict real estate values based on year data. This model was built using Python and deployed via a Flask API, enabling predictions through API requests. Tools used: • Python • Scikit-learn • Flask API • NumPy • Postman This project explores the integration of machine learning models into APIs for real-world prediction systems. It has been a valuable learning experience while experimenting with @Uptor. #MachineLearning #Python #FlaskAPI #DataScience #AI #Learning

🚀 Day 6 of My AI/ML Learning Journey | Diving Deeper into Python 🐍 Every day of learning Python is unlocking a new layer of understanding. Today’s focus was on File Handling, Exception Handling, and efficient Python techniques that make programs more robust and practical. 💻✨ 📚 Topics Covered Today: 📂 File I/O in Python 🛠 Operations on Files 🔑 File Modes (read, write, append, etc.) 🤝 Using the with keyword for safer file handling 🗑 Deleting Files 🧩 Practice Problems ⚠️ Exception Handling 🔚 finally Keyword ⚡ List Comprehensions 📄 Working with JSON Module 💡 Key Takeaway: Understanding file handling and exception handling makes programs more reliable and production-ready, while techniques like list comprehensions help write clean and efficient code. Small progress every day → Big transformation over time. 🚀 Still going strong on my #100DaysOfCode journey. #AI #MachineLearning #Python #CodingJourney #100DaysOfCode #LearningInPublic #BuildInPublic #Consistency

🗓️Day 199 of 365 Days I went through a Python cheat sheet to quickly revise the most important concepts. Reviewed: 🔹 Basic syntax and data types 🔹 Loops and conditional statements 🔹 Functions and list/dictionary operations 🔹 String handling and common built-in methods This quick recap helped refresh key concepts and improve recall speed. Strengthening the fundamentals before moving into deeper AI and ML topics 🚀 #365DaysOfCode #Day199 #Python #Revision #CheatSheet #LearningJourney #Consistency

I picked up Python this week. Learning AI is one of the key skills I plan to add to my portfolio, and Python sits right at the foundation of that journey. Yes, there are plenty of documentation tools out there. But I want to build custom automation tools that work specifically for the products I document. I plan to combine Python fundamentals with AI to build smarter, more intentional tools for documentation. #TechnicalWriting #Python #AI #Documentation #LearningInPublic

🧠 Why Strong Python Basics Matter in AI Many beginners jump directly into TensorFlow or PyTorch. But I realized something important: Without strong Python fundamentals: • Debugging becomes difficult • Writing custom logic is hard • Understanding model flow becomes confusing Now I’m spending time improving: ✔ Functions ✔ OOPS ✔ Loops and conditions ✔ Algorithm thinking AI is powerful. But fundamentals build confidence. #Python #AI #MachineLearning #CodingJourney

📊 Car Price Prediction using Linear Regression Built a simple machine learning model to understand how mileage and age impact car prices. 🔹 Used Python, Pandas, NumPy & Scikit-Learn 🔹 Performed train–test split for evaluation 🔹 Visualized the negative relationship between mileage and price Small steps, consistent learning 🚀 #MachineLearning #Python #DataScience #LinearRegression #LearningByDoing #MLBeginner

What’s the biggest dataset you’ve ever scraped? Curious about this. A lot of scraping discussions focus on techniques, but not much on scale. What’s the largest dataset you’ve personally collected from scraping? Thousands of pages? Millions? Also curious what problems started appearing once things got bigger. #WebScraping #BigData #DataEngineering #Python #DataScience #WebData

Ever wondered how to plan your week to study smarter, not harder? 🤔 I built my first AI Study Planner using Python! ✅ What it does: Takes your subjects, difficulty, importance, and available hours Calculates a priority score for each subject Suggests how much time to spend on each topic Visualizes priorities and hours with charts 🔹 Why it’s human-like: I added a bit of randomness in suggested hours to make it feel like a student experimenting with their study schedule. 💡 What I learned: Data preprocessing using pandas Normalizing values with scikit-learn Visualizations using matplotlib & seaborn Making a project interactive and human-friendly 💻 Try it yourself: Check the code & README: https://lnkd.in/guEREPrU Would love your thoughts or suggestions to make it even smarter! #Python #MachineLearning #AI #StudentProject #PortfolioProject #LearningByDoing #DataScience #StudyPlanner

Building AI apps with LangChain ❓ Then understanding its core components isn’t optional — it’s foundational. I’ve just published a hands-on guide explaining LangChain with practical Python examples, covering: → Models → Messages → Tools → Agents → Memory → Streaming → Structured Output → Middleware I break down each of these in a practical LangChain guide — with working Python code. This is implementation focused, not theory. Full article in the first comment 👇 #LangChain #AIEngineering #Python #LLM #GenerativeAI

Andrej Karpathy published microgpt.py — a GPT in 200 lines of pure Python. No PyTorch. No dependencies. Just raw autograd and attention built from scratch. It's a useful way to see exactly what's happening inside a language model: the matrix multiplications, the gradient flow, the attention mechanism in actual code. We walked through it for developers — what each function does, why it works, and when you'd build from scratch vs call an API. Full breakdown: https://lnkd.in/gmNbi5kQ