Built a Voice Classification and Clustering Model with Python and ML

Fake News Detection using Machine Learning  I built a Fake News Detection model that classifies articles as Real or Fake using Python ,Scikit-learn and TF-IDF Vectorizer.   – Data preprocessing & feature extraction using TF-IDF   – Logistic Regression for classification   – Achieved ~95 % accuracy on test data   – Implemented in Google Colab and uploaded on GitHub   Project Link: [https://lnkd.in/gEqUfWfc)   #MachineLearning #AI #Python #DataScience #FakeNewsDetection #MLProjects #GitHub

📘 Resource Recommendation: Understanding Vector Embeddings in AI A very insightful session by Pamela Fox that demystifies vector embeddings and their role in modern AI systems. 🎥 Watch here: https://lnkd.in/e9mwTMdA In just one hour, the session covers: 🔹 How vector embeddings work across models 🔹 The idea of similarity space 🔹 Vector search — Exhaustive vs ANN (HNSW, DiskANN) 🔹 Quantization (Scalar, Binary) 🔹 MRL dimension reduction 🔹 Compression with rescoring The accompanying Python notebooks allows for practical experimentation — ideal for those who want to go beyond theory. This session is part of the broader Python + AI series. You can explore more recordings here: 📌 https://aka.ms/PythonAI/2 #AI #MachineLearning #Python #VectorSearch #Embeddings #MicrosoftAI #TechLearning

🐍 Python: The Backbone of Modern AI & Data Science From data manipulation to deep learning, Python's ecosystem powers the entire AI pipeline. Here's a visual breakdown of the core libraries every data professional should know: 📊 NumPy - Fast numerical operations 🐼 Pandas - Powerful data manipulation 📈 Matplotlib - Beautiful visualizations 🤖 Scikit-learn - Classical ML algorithms 🔥 PyTorch - Dynamic deep learning 🧠 TensorFlow - Production-ready AI Which library do you use the most in your projects? Drop a comment below! 👇 #Python #ArtificialIntelligence #MachineLearning #DataScience #AI #DeepLearning #TechCommunity

Want to code Logistic Regression from scratch without relying on libraries? In my latest video, I break down the math, derive the gradient descent update rules, and implement the entire model step by step in Python. Perfect for anyone looking to understand the core logic behind ML algorithms or preparing for interviews. Video Link: youtu.be/cT_U40djaww   Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

A quick visual reference covering some of the most essential functions and classes in the scikit-learn library — from data preparation to model evaluation Each tool serves a specific role: 😎 These functions form the foundation of efficient, reliable, and reproducible ML workflows. #MachineLearning #DataScience #Python #ScikitLearn #AI #ModelEvaluation #Analytics

#Day29 of #100DaysOfCode Today I learned about the Bias–Variance Tradeoff in Machine Learning. High Bias → Underfitting (model too simple) High Variance → Overfitting (model too complex) The goal is to find the right balance for best accuracy ✅ Understanding this tradeoff helps in building models that generalize well on unseen data. #MachineLearning #DataScience #AI #Python #LearningJourney #100DaysOfCode

Python: The Language Powering the AI Revolution Python has become the backbone of modern Artificial Intelligence. Its simplicity, readability, and vast ecosystem of libraries — like TensorFlow, PyTorch, and scikit-learn — make it the go-to language for data scientists, researchers, and AI engineers worldwide. From rapid prototyping to deploying production-level models, Python bridges the gap between human creativity and machine intelligence. It’s not just a programming language — it’s a tool enabling the next generation of innovation. #Python #ArtificialIntelligence #MachineLearning #DataScience #Innovation

Data analytics lays the foundation — mastering SQL, Python, and visualization teaches us how to interpret information. AI builds on that foundation — using machine learning and automation to make systems smarter and more adaptive. It’s fascinating how the same data that once told a story can now drive decisions on its own. That’s the true evolution — from analyzing patterns to building intelligence. #DataAnalytics #ArtificialIntelligence #MachineLearning #CareerGrowth #Python #DataScience #AI #Analytics #ContinuousLearning #TechTransformation

Week 5 of my AI & Data Science journey 🚀 This week, I explored Python Memory Management — a crucial concept for writing efficient and scalable programs. Key learnings: Understanding how Python allocates and manages memory Exploring the heap, stack, and reference counting mechanism Working with the garbage collector (gc module) Analyzing memory leaks and optimization techniques for data-heavy applications Efficient memory handling is key to ensuring ML models and data pipelines run smoothly — especially when working with large datasets. 📂 Notes & Assignments: https://lnkd.in/gPnQkhGY #Python #DataScience #AI #MachineLearning #MemoryManagement #LearningJourney #CodeOptimization

🚀 15 Python Libraries Every Data Scientist Must Know! From Numerical Computing (NumPy) to Deep Learning (PyTorch) and Web Development (Flask) — these libraries make Python the heart of Data Science. 💡 Upskill with AimNxt and build real-world AI solutions! #DataScience #MachineLearning #Python #AI #DeepLearning #AimNxt #TechSkills