Codeayan’s Post

Unpopular opinion: Most ML portfolios are useless. 10 Titanic survival predictions. 5 house price regressions. 3 MNIST digit classifiers. Everyone has the same projects because everyone follows the same tutorials. Recruiters have seen it 1000 times. The projects that actually stand out solve a real problem with messy real-world data. Not clean Kaggle datasets with a leaderboard. What’s a project you’ve seen that actually impressed you? #MachineLearning #AI #Python #ComputerVision #StudentDeveloper #BuildInPublic #DeepLearning #DataScience #PyTorch #Programming

Most people think machine failure is unpredictable. It is not. Machines give warnings before they fail through temperature, vibration , speed and torque. The data is there. The question is whether anyone is listening. I spent the last few weeks building a system that listens. Using 10,000 industrial sensor readings, I trained a model that predicts machine learning failure before it happens. Not because it was a university assignment. Because I wanted to understand how AI actually works in a factory. #Machinelearning #PredictiveMaintenance #ArtificialIntelligence #IndustrialAI #Python

RDKit Convert SMILES into molecules Generate molecular fingerprints Calculate descriptors for QSAR Clean/standardize chemical structures Prepare features for ML models RDKit helps convert chemistry into structured data you can use in ML. #AI #Python #DrugDiscovery #ComputationalChemistry

🤖 Machine Learning is shaping the future. From data to decisions, from code to intelligence. The world is moving towards automation and smart systems. Learning technologies like Python and Machine Learning is no longer optional — it’s the future. 🚀 Start today, stay ahead tomorrow. #MachineLearning #AI #Python #Technology #Future #Learning

Most tutorials teach you to build a model. Nobody teaches you what to do when it breaks in production. Here’s what actually goes wrong after deployment: → Input data format shifts slightly and your preprocessing crashes → A class your model never saw during training starts appearing → Confidence scores are high but predictions are wrong → Model works on your machine. Fails on the server. These aren’t ML problems. They’re software engineering problems. The gap between “model works in notebook” and “model works in production” is where most ML beginners get stuck. Bridging that gap is the actual skill nobody talks about. What’s the messiest production bug you’ve encountered? #MachineLearning #MLEngineering #Python #DeepLearning #SoftwareEngineering #ComputerVision #PyTorch #MLOps #AI #Programming

Machine Learning/Artificial Intelligence  Day 6 Today, I focused on understanding functions in Python ,a key concept for writing organized and reusable code. I learned how functions allow us to group logic into reusable blocks, making programs more efficient and easier to manage. Instead of repeating code, functions help simplify complex tasks and improve readability.In AI/ML, this becomes essential because:· Model training logic can be wrapped into functions· Data preprocessing steps become reusable· Hyperparameter tuning gets cleaner and more modularThis is an important step toward building scalable programs , because AI/ML isn't just about getting results, it's about writing code that others (and your future self) can understand and build upon.Learning step by step. Staying consistent every day.#M4ACE LearningChallenge #LearningInPublic #Python #Functions #AI #MachineLearning

What if you could improve LLM outputs without training a single parameter? InferScale 0.1.3 makes that possible. By generating multiple outputs and selecting the best, it increases the probability of high-quality responses efficiently. This method works across use cases like paraphrasing, information extraction, and QA. It’s a practical solution for teams that want better results without scaling infrastructure or costs. Inference-time scaling isn’t just a trick—it’s a strategy. Learn more: https://lnkd.in/g8MDkbEZ #AI #MachineLearning #LLM #DataScience #Python #OpenSource #Innovation

Everyone says “learn AI” But no one tells you WHAT to learn Here’s the actual stack 👇 🐍 Programming Language Start with Python Example: Easy syntax Example: Huge AI community 📚 Libraries These do the heavy lifting Example: TensorFlow Example: PyTorch 📊 Data Handling You need to work with data Example: Pandas Example: NumPy 📈 Visualization Understand what your model is doing Example: Matplotlib Example: Seaborn ⚙️ Tools & Platforms To build and run models Example: Jupyter Notebook Example: Google Colab ⚠️ Reality: You don’t need EVERYTHING Start small → go deep 🧠 Focus > Overwhelm Master basics first 🔜 Next: How AI is evolving (future + trends) #AI #ArtificialIntelligence #MachineLearning #Python #Developers #Coding #DataScience #Tech #LearnAI #SoftwareEngineering

Fun way to show AI and Python fighting 😄 but in real life, they are not enemies—they actually work together and make things smarter. . . . . #AI #Python #Artificial intelligence #Machine learning #Coding #Tech #Programming #Data science #Innovation #Future tech #Learning #Developers #TechLife

🚀 Excited to share my AI & ML Practicals Repository! I’ve uploaded my Artificial Intelligence and Machine Learning (AIML) lab practicals on GitHub, covering key concepts like data preprocessing, EDA, supervised & unsupervised learning algorithms, and model evaluation. 🔍 This repository reflects my hands-on learning in Data Science and helps strengthen my practical understanding of machine learning concepts :🔹 DataFrame Operations 🔹 Correlation Matrix 🔹 Normal Distribution 🔹 Simple Linear Regression 🔹 Logistic Regression 🔹 Decision Trees (ID3 Algorithm) 🔹 Confusion Matrix 🔹 Decision Tree Pruning A special thanks to my mentor Ashish Sawant for guiding us throughout this practical sessions !! 📂 Check it out here : https://lnkd.in/g7f3Q8vv I’d love your feedback and suggestions! 😊 #MachineLearning #ArtificialIntelligence #DataScience #Python #GitHub #LearningJourney