Mohammad Aiman’s Post

Machine Learning Image Data using scikit image #machinelearning #datascience #dataimage #scikitimage scikit-image (formerly scikits.image) is an open-source image processing library for the Python programming language.[2] It includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more.[3] It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The scikit-image project started as scikits.image, by Stéfan van der Walt. Its name stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy.[4] The original codebase was later extensively rewritten by other developers. Of the various scikits, scikit-image as well as scikit-learn were described as "well-maintained and popular" in November 2012.[5] Scikit-image has also been active in the Google Summer of Code. https://lnkd.in/gvmx22q2

🚀 Built a Machine Learning model that predicts house prices. Most people stay stuck in tutorials. I decided to apply it. Used Linear Regression to train on real housing data, evaluated performance, and saved the model for reuse. 📊 Results: • R² Score: 0.58 • MSE: 0.56 Not perfect, but real learning happens here building, testing, improving. Pushed the complete project to GitHub 💻 #BuildInPublic #MachineLearning #AIJourney #Python #DataScience #Consistency #KeepLearning

🚀 Day 1: The Foundation & The "Aha!" Moment Focus: Variables & Fundamentals Today, I officially wrote my first lines of Python after a long time, and let’s just say… it was a beautifully humbling experience. 😅 I realized that even the "simplest" tasks—like adding numbers or printing a sentence—require total precision. Python is gentle until you forget a single quote mark, and then it’s game over! What I tackled today: The Execution Flow: Understanding how Python reads code from top to bottom. The Power of Variables: They’re essentially just containers, but naming them is an art form. The Rules: Learned why (my_var) works, but (2_var) breaks everything. Data Types: Realizing that 10 (integer) and "10" (string) might look the same to us, but they are worlds apart to a computer. It’s exciting, a little frustrating, and 100% worth it. 😁 #Python #Day1 #LearningToCode #TechBeginner #CodingFromScratch

Day2 & 3✅️ What's harder — learning Python or just showing up every day? Asking because Day 2 & 3 just became one post. Not perfect but not quitting either. 🤷♀️ Here's what got done: 🔁 Loops — for, while, nested. Practiced with real examples. 🔀 Conditionals — if, elif, else. Sounds easy until it isn't. 🧩 Functions — where it finally started feeling like actual coding. 📦 Data Structures — Lists, Tuples, Sets, Dictionaries. All of them. Done. Two days. Lots of concepts. Some confusion. More clarity. Day 4 is already in progress — dropping that tomorrow. 👀 #Python #LearningInPublic #100DaysOfCode #DataAnalytics #PythonForDataScience #CSEStudent

Organizing Data Lists & Tuples Day 5: If variables are containers, today I learned how to build a warehouse. I dove deep into Lists and Tuples. Lists: Perfect for items that need to change, grow, or be sorted (Mutable). Tuples: The "set it and forget it" version (Immutable). Learning how to slice through a list felt like a superpower. It’s amazing how much more organized your logic becomes when you know how to group your data properly. A Python tuple is an ordered and immutable collection of elements enclosed in parentheses, commonly used for grouping related data that should not change. #DataStructures #PythonLists #LearningInPublic #TechJourney #Pythonday5

Most Python workflows rely on heuristics. They’re quick, intuitive, but usually not optimal. A simple greedy approach might get you a solution, but it often leaves efficiency, performance, and cost savings on the table. GAMSPy brings algebraic modeling into Python, so you can express constraints and objectives directly and solve for a true optimum. At PyConDE & PyData 2026, Justine Broihan and Muhammet Soyturk will walk through this using a classic operations example, and then extend it into machine learning. They'll cover: 🔸 How optimization compares to rule-based heuristics and 🔸 How it can be used to test ML models (e.g. minimal changes needed to trigger misclassification) 🔸 The Art of the Optimal: A Pythonic Approach to Complex Decision-Making 📍 April 14 · 16:30 📍 Platinum (2nd Floor) If you're building decision-making systems in Python, this is worth a look. More details 👉 https://lnkd.in/dyifGdVi #PyConDE #PyData #Optimization #GAMSPy #GAMS #Python

Weekly Challenge 11: K-Nearest Neighbors You don't always need massive libraries like scikit-learn to do Machine Learning. Sometimes, the best way to truly understand an algorithm is to build its core logic yourself! For Week 11 of my Python coding challenge, I implemented the K-Nearest Neighbors (KNN) algorithm purely with math and Python. KNN is essentially a voting system based on proximity. 1 A new, unknown data point enters the space (the green star). 2 We calculate the Euclidean distance to EVERY other point. 3 We find the "K" closest neighbors (in this case, 5). 4 The neighbors vote! If the majority are Blue, the new point becomes Blue. It’s a beautiful mix of geometry, sorting algorithms, and data structures. I used Matplotlib to visualize how the algorithm "connects" the unknown point to its closest peers to make a decision. Full source code on my GitHub: https://lnkd.in/eV-FieS2 #MachineLearning #Python #DataScience #ArtificialIntelligence #KNN #Algorithms #CodingChallenge #UANL

Been learning Data Analytics for the past few months. One thing is clear: numbers aren’t optional — they are the core. Everything in analytics revolves around how efficiently you can process, manipulate, and extract meaning from data. That’s where NumPy comes in. Built on C, it’s significantly faster and more efficient than plain Python for numerical operations — often by huge margins. If you’re still relying only on Python loops, you’re doing it wrong. Sharing a quick NumPy cheat sheet I’ve been using to level up my workflow. Stop writing slow code. Start thinking in arrays. #DataAnalytics #DataScience #Python #NumPy #MachineLearning #AI #Programming #DataAnalysis #LearnDataScience #Upskilling #CareerGrowth #CodingLife #BuildInPublic

Solved a nice Binary Tree Problem today: Print all root to leaf paths. The approach is really simple: we'll traverse the tree and store all paths that start at the root and end at a leaf node. So here’s what I did for this problem: • Used recursion with DFS to traverse the tree • Used a path list to keep track of all paths • Whenever I encountered a leaf node, I pushed it into the answer list • Once I visited a node, I removed it from my path list to backtrack and explore all paths An interesting thing I observed while solving this question: Since lists in Python are mutable references, if we push the path into the answer list, it will change all paths in the answer list. Hence, we push a copy of the path into the answer list. This is another nice example of how we use DFS and backtracking for trees. These kinds of small questions really help us solidify our recursion intuitions. #DSA #BinaryTree #Recursion #CodingPractice

Python is more than just code; it’s a powerful calculator! 🧮 Today, while diving deeper into my Data Science journey, I spent some time mastering Python's mathematical operators. It’s not just about simple math; it's about understanding how the machine processes different operations to build solid business logic. From basic addition to Floor Division and Exponentiation, understanding these basics is crucial for building accurate data models later on at Data Hub. 📊 In this snippet: Handled different types of operations. Explored how Python handles float results vs integers. Question for the experts: What’s the most common mathematical error you faced when you first started coding? 🧐 #DataHub #Python #Coding #DataAnalysis #LearningJourney #TechCommunity