NTQR: Unsupervised Classifier Evaluation in Python

🚀 Day 21 of My Generative & Agentic AI Journey! Today’s focus was on understanding how to import functions and modules in Python — an important step towards organizing code in real-world projects. Here’s what I learned: 📦 Importing Modules: • We can import an entire module and access its functions using dot notation 👉 Example: import math Using functions like math.sqrt(), math.floor() 📥 Importing Specific Functions: • Instead of importing everything, we can import only required functions 👉 Example: from math import sqrt, ceil 👉 Makes code cleaner and avoids unnecessary imports ⚠️ import * (Not Recommended): • Using import * brings all functions and variables into the current namespace • Can cause confusion and naming conflicts 👉 Better to explicitly import only what is needed 💡 Key takeaway: Proper use of imports helps in writing modular, clean, and maintainable code — especially in large projects. Taking one more step towards writing structured and scalable applications 🚀 #Day21 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

Built a Machine Learning API using FastAPI I developed a machine learning-based API that predicts salary based on user input level. My all project and machine learning model based API on github. GitHub : https://lnkd.in/gR_qsxwM 🔹 Implemented Machine Learning algorithms and integrated them with FastAPI 🔹 Enabled real-time prediction using API based on user input 🔹 Designed RESTful endpoints for seamless interaction 🔹 Stored and retrieved prediction data dynamically 💡 This project demonstrates how ML models can be deployed and used through APIs in real-world applications. Tech Stack: Python, FastAPI, scikit-learn #MachineLearning #FastAPI #Python #DataScience #AI #BackendDevelopment #MLProjects

🚀 Machine Learning With Python From Scratch Part 3! This one is about something every ML beginner struggles with — One Hot Encoding. Machine learning models only understand numbers. So what do you do when your data has text like "BMW X5" or "Audi A5"? You convert it. One Hot Encoding turns each category into its own column of 1s and 0s. Simple idea, but if you do it wrong your model breaks and most beginners don't even know why. There's also a trap that nobody warns you about, the Dummy Variable Trap. When you have 3 categories, you only need 2 columns. The third one is redundant and adds noise to your model. I cover exactly how to avoid it. In this notebook I cover two ways to do it: pd.get_dummies — quick and simple Sklearn's OneHotEncoder with ColumnTransformer — the proper production way Both approaches are used to predict car sell prices based on brand, mileage and age. 🔗 Full notebook + dataset + detailed explanation on GitHub: 👉 https://lnkd.in/dC5Pzygv Follow along, building this series one concept at a time, from scratch. #MachineLearning #Python #DataScience #OneHotEncoding #FeatureEngineering #GitHub #BeginnerML #100DaysOfCode

A simple problem maybe more complex than expected. Still not getting it right after more than 20 iterations, I mean with realistic and random knob/tab shape. Tried a few LLMs. Question: given a photo folder create Python code to generate a jigsaw puzzle like patchwork from these pictures. Here is my latest test, feeding outputs between tests. First iterations were about basic knobs/tabs shapes creation. Getting realistic knob/tabs shapes took more work. Mismatch between boundary shapes and pictures orientation not respected. The latest generated code now has three passes. You may to try on your side. #ArtificialIntelligence #MachineLearning #LargeLanguageModels #LLM #PythonDevelopment #SoftwareEngineering #ComputerVision #ImageProcessing #GenerativeAI #AIEngineering #PromptEngineering #AlgorithmDesign #DataScience #DeepLearning #TechInnovation #ProblemSolving #SoftwareDevelopment #CodingLife #Debugging #EngineeringChallenges #RAndD #AppliedAI #AIProjects #OpenSourceAI

Smarter LLM outputs don’t always require bigger models. Introducing Inferscale 0.1.1—a lightweight Python package that improves response quality using inference-time scaling techniques. Designed for developers who want better results without increasing compute costs, Inferscale focuses on practical gains you can integrate quickly into your workflows. If you're building AI products, experimenting with prompts, or optimizing pipelines, this is worth exploring. Check out the README: https://lnkd.in/giq8KJ5g Let’s make LLMs more efficient together. #AI #LLM #Python #GenAI #MachineLearning #OpenSource #AIDev

✨ A New Beginning in My AI/ML Journey As part of the Industry Immersion Program by MeetMux, Day 3 marked my transition from setup to execution. 🔹 What I tackled today: Built a basic data pipeline using Python, NumPy, and Pandas — focusing on how data is processed, structured, and analyzed. 🔹 What I learned : The concept of vectorization in NumPy — instead of using loops, operations can be applied to entire datasets at once, making computations significantly faster. This is a core technique used in real-world AI systems. 🔹 My goal: To continue building a strong foundation in data handling and move towards implementing real-world machine learning models by the end of this week. 🔗 My Work (GitHub): https://lnkd.in/gQNYJ8ce #AI #MachineLearning #Python #NumPy #Pandas #IndustryImmersion #LearningInPublic #MeetMux

𝗧𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗹𝗼𝗼𝗸𝘀 𝘀𝗶𝗺𝗽𝗹𝗲... 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝘁𝗿𝘆 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆. Most tutorials stop at fitting ARIMA or Prophet on a clean dataset. In practice, it’s messier: – missing data – leakage – feature engineering – evaluation pitfalls – deployment constraints That’s where most real-world projects fail. There’s an upcoming workshop that focuses exactly on this gap — end-to-end forecasting in Python, not just models in isolation. What I like about it: – covers the full pipeline (data → features → models → evaluation) – hands-on, not just theory – focused on practical decisions you actually face If you're working with time series (or planning to), this is a useful one to check. 👉 https://lnkd.in/dnergVrT Use code 𝗔𝗡𝗗𝗥𝗘𝗬𝟰𝟬 for 40% off. Event date: May 2 #MachineLearning #TimeSeries #DataScience #Forecasting #MLOps

🚀 Day 19 of My Generative & Agentic AI Journey! Today’s focus was on exploring different types of functions in Python and how they are used in real-world programming. Here’s what I learned: ⚙️ Pure vs Impure Functions: • Pure Functions → Always return the same output for the same input and don’t modify external data 👉 More predictable and easier to test • Impure Functions → Depend on or modify external variables 👉 Less predictable, generally avoided in clean code 🔁 Recursive Functions: • A function that calls itself to solve a problem step by step 👉 Example use case: Breaking a problem into smaller parts (like factorial, countdown, etc.) ⚡ Lambda (Anonymous) Functions: • Small, one-line functions without a name • Useful for short operations where defining a full function is unnecessary 👉 Example use case: Quick calculations or transformations 💡 Key takeaway: Understanding different types of functions helps in writing cleaner, efficient, and more maintainable code. Slowly moving towards writing optimized and professional-level Python 🚀 #Day19 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🚀 Excited to share my latest project: AI Log Analyzer I built a web-based application using Python and Streamlit that can: ✔ Upload and analyze log files (.txt / .log) ✔ Classify logs into ERROR, WARNING, INFO, CRITICAL ✔ Visualize log distribution with graphs 📊 ✔ Search logs instantly 🔍 ✔ Generate downloadable reports 📄 ✔ Predict log type using Machine Learning 🤖 🌐 Live Demo: https://lnkd.in/gTNK_NQ5 This project helped me strengthen my skills in Python, data analysis, and basic machine learning using libraries like scikit-learn and matplotlib. Looking forward to exploring more real-world AI applications and improving this project further! #Python #MachineLearning #Streamlit #AI #DataScience #Projects #GitHub #Learning #Developer

I’m excited to share a new project I’ve been working on: an Iris Flower Classification system built using Python and Scikit-learn! 🧠 This project focuses on the fundamentals of Supervised Learning. By training a model on sepal and petal measurements, I was able to successfully classify three different species of Iris flowers with high precision. Key Highlights: Algorithm: Implemented K-Nearest Neighbors (KNN) for classification. Preprocessing: Used StandardScaler for feature scaling and split the data for robust testing. Performance: Achieved an accuracy of 95% - 100% on the test set. 📈 Working on this reinforced my understanding of the machine learning workflow from data loading and preprocessing to model evaluation using classification reports and confusion matrices.CodeAlpha You can check out the full code and README on my GitHub here: [ https://lnkd.in/gJcYm7dF ] 🚀 #MachineLearning #DataScience #Python #ScikitLearn #AI #CodingJourney #GitHub #Classification #CodeAlpha