Agent-Led Edge Inference with Python and scikit-learn

Built a Movie Recommender System using Machine Learning to understand how large-scale platforms personalize content. . . . Worked on data preprocessing, feature representation, and similarity-based recommendations using Python and Scikit-learn. Developed in Colab, modularized and tested in PyCharm. #MachineLearning #Colab #MovieRecommenderSystems #DataScience #Python #MLProjects #LearningByBuilding

Agent-Driven QuOps Metrics. Tracking logical accuracy vs. noise levels via autonomous monitoring agents. Skills: scikit-learn, Python. https://lnkd.in/emtA2Fat #Monitoring #Performance #AgenticAI

Learning Update | Python for Generative AI Today, I revisited key Python concepts essential for Machine Learning and Generative AI and organized my progress into a structured GitHub repository. The repository covers Python libraries, statistical analysis (univariate, bivariate, multivariate), and core Python concepts from an ML/GenAI perspective. I’m looking forward to continuously learning and updating this repository as I grow in the field. Sharing my learning progress here: 🔗 GitHub repository link https://lnkd.in/gHaZa3Zf #Python #MachineLearning #GenerativeAI #LearningInPublic #GitHub

🚢 Titanic Survival Prediction – Machine Learning & Streamlit ✅ Developed and deployed a user-friendly Streamlit web application to predict Titanic survival using Machine Learning, with clear model comparison and performance insights GITHUB LINK : https://lnkd.in/gaptG8kv STREAMLIT.IO LINK : https://lnkd.in/g6R6TwAd #DataScience #MachineLearning #Streamlit #Python #MLProject #LearningJourney #CareerRestart

Neural Network. Takes two inputs, adds them together, applies sigmoid activation, prints input, sum and output. Python package Numpy.

Day 14 – Weekly reflection ✅ This week I focused on: • Understanding AI vs ML vs DL • Data basics with Python • Maintaining daily consistency Next week: more practice and mini projects. #WeeklyReflection #AIJourney #Consistency

Cluster-Based QML Inference. Goal-oriented agents managing real-time prediction fixes on federated multi-QPU edge nodes. Skills: Python, scikit-learn. https://lnkd.in/dR837zSA #Branch51 #EdgeAI #QuantumInference

Partitioned Feature Mapping. AI agents autonomously distributing feature-encoding tasks across networked QPUs for faster processing. Skills: Python, scikit-learn. https://lnkd.in/dR837zSA #Branch51 #ParallelComputing #QML

Supervised and Unsupervised Learning. With Supervised learning there is a definite outcome, whereas Unsupervised learning the whole point is to see what you have, and try to make sense of it. #data #python #learning

AI agents simulating clinical trial failures using synthetic patient cohorts to mitigate investor risk. Skills: QuantumAlgorithms, Python. https://lnkd.in/dip82gj7 #RiskManagement #MacroFinance #AgenticAI