Ordinal vs Label vs One-Hot Encoding in Machine Learning

Turning research papers into working ML systems 📄➡️💻 Rebuilding concepts like positional encoding & transformers from scratch to master the fundamentals 🧠 Hands-on always beats tutorials 🔥 If you want to really learn ML, this is the way. 🔗 Explore more at Tensor Tonic Innomatics Research Labs #ArtificialIntelligence #MachineLearning #ResearchToCode #Transformers #LLMEngineering #AIBuilders #DeepLearning #Python #DataScience

🚀 Text Summarization using Python Transformers I explored the Transformers library in Python and implemented text summarization using the pre-trained T5ForConditionalGeneration model. The T5 model follows a text-to-text approach, making it very effective for tasks like summarization, translation, and question answering. 🔹 Model: T5 (t5-small) 🔹 Task: Text Summarization 🔹 Library: Hugging Face Transformers This was a great learning experience in Generative AI. #Python #Transformers #T5 #MachineLearning #GenerativeAI

🚀 Day 4/15: Intermediate to Advanced Python for ML/DL/AI Projects 🐍 Forgotten cleanups causing leaks in your training runs? 😤 Today: Context Managers & contextlib — automatic resource handling that always works. Beginner-friendly breakdown + 5 interview questions with code → swipe the carousel! Save if you're writing robust pipelines! What's your go-to context manager trick? Tell me below 👇 #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps #Python #MachineLearning #DeepLearning #AI #DataScience #MLOps

🐍 Python Quiz: Do you actually know how yield works? Take a look at the code snippet below. Without running it in your IDE or asking an AI, what is the exact output? def countdown(n):   while n > 0:     yield n     n -= 1 for x in countdown(3):   print(x, end=' ') The Rules: 🚫 No Google. 🚫 No AI. 🧠 Use your brain. The Real Challenge: Don't just post the answer—explain HOW the answer came to be in the comments. Specifically, where did the next() call go? #Python #CodingChallenge #SoftwareEngineering #PythonProgramming

🚀 Day 38 – Machine Learning Journey Cross Validation (Python Implementation) Today I implemented Cross Validation in Python to evaluate models more reliably instead of trusting a single train–test split. 🔹 Why in code Measures consistency, not luck Reduces overfitting bias Gives a stable performance estimate 🐍 Minimal Python example (scikit-learn) from sklearn.model_selection import KFold, cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() kf = KFold(n_splits=5, shuffle=True, random_state=42) scores = cross_val_score(model, X, y, cv=kf) print(scores.mean()) 🔹 What this does: Splits data into 5 folds Trains on 4, validates on 1 Repeats for all folds Reports the average score 🔑 Key takeaway: > Cross validation doesn’t change the model — it validates whether the model can be trusted. #Day38 #MachineLearning #CrossValidation #Python #ScikitLearn #LearningJourney #ModelEvaluation #DataScience #AI #LearningJourney

Hello Everyone, My First Video in the Python + AI Series is Live [AI PDF Summarizer Using Python]! AI is everywhere — but most people think it’s too complex or requires heavy ML & math. So I started a Python AI Series where I focus on: ✅ Practical use cases ✅ Clean Python code ✅ Real-world automation ✅ Beginner-friendly explanations 🎥 In my first video, I show how to: 👉 Build an AI-powered PDF Summarizer using Python 👉 Understand how AI models work in the background 👉 Control cost, performance, and architecture 👉 Use AI without machine learning or data science This series is for: 1. Python beginners 2. Automation engineers 3. Students & working professionals Anyone curious about AI but unsure where to start 📌 This is just the beginning — next videos will be more exciting ! 🔗 Watch the video here: https://lnkd.in/dBiSsADm If you’re learning Python or planning to move into AI — this series is for you. #Python #ArtificialIntelligence #PythonAI #Automation #AIProjects #LearningByBuilding #TechContent #DeveloperJourney

Day 16 of 40: Teaching AI to Write Python 🐍 We all know LLMs aren't great at math—they try to predict the next word instead of calculating the answer. Today, I fixed that by giving my AI a Code Execution Tool. Instead of guessing, my agent now: Detects a computation problem. Writes a Python script dynamically. Executes it in a sandbox. Returns the mathematically proven answer. It’s no longer just a language model; it's a logic engine. Tech Stack: Python, Google Gemini 2.0, Code Execution Tool. #GenerativeAI #AgenticAI #Python #Gemini #Engineering #Day16

Build a face recognition system in 5 lines of Python 🎯 Here's the code: import cv2 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') video = cv2.VideoCapture(0) while True:   ret, frame = video.read()   faces = face_cascade.detectMultiScale(frame, 1.3, 5)   cv2.imshow('Face Detection', frame) This is literally HOW we built Nova Teach 🚀 Want the full tutorial? Follow for more 👇 Full code + explanation in our blog (link in profile) #Python #OpenCV #FaceRecognition #AI #Coding

🔹 Practicing Generative AI – Text to Text Implemented a text-to-text pipeline using Gemini models with Python. Focus was not just on generating text, but on understanding: • how prompts are processed • how responses are structured • how modern GenAI SDKs are designed (stateless requests) Tech stack: Python · Gemini SDK · dotenv Next step: connecting this to real use cases like chat & RAG 🚀 #GenerativeAI #Gemini #Python #AIEngineering

I really feel that vibing in Rust is more productive (hello statically typed language?) and less annoying for me than in Python. Codex + GPT5.2 (xhigh) just does Rust right for me. Python falls apart and breaks every other feature implementation. #Rust #Python #Vibing #Productivity