Here’s a new beginner-friendly tutorial I wrote on Geo AI for Industrial Engineering using Python. It walks through a simple hands-on mini-project: preparing location data, running light clustering, and visualizing the results on an interactive map. The goal is to make Geo AI feel practical and approachable, especially for students and early learners who want to see how spatial intelligence can support real decision-making. A good reminder that sometimes the best way to understand a new concept is not to start with heavy theory, but to build something small that makes the idea visible. https://lnkd.in/gTAs_5Bb #GeoAI #IndustrialEngineering #Python #DataVisualization

AI Bootcamp Diaries Day 1: After the introductory session and essential housekeeping, we dived right in to the fundamentals of Python. The session was mostly a revision of basics of the language such as: * Variables, * Data structures in Python (integer, float, string, list, tuple, and dictionary), * Printing f-strings for elegant handling of variables inside string literals. * Mathematical operators to perform mathematical operations, * Comparison operators, * Logical operators. #LifeLongLearning, #AI, #Python

Today, I started diving into the basics of Python, the programming language at the heart of AI and Machine Learning. I explored different data types like integers, floats, booleans, complex numbers, and strings, and learned the rules for using parentheses and other syntax essentials. My Key Takeaways: Choosing the right data type is critical for correct operations Understanding Python syntax ensures your code runs smoothly These foundational concepts make everything else in AI/ML easier to learn Python may seem simple at first glance, but mastering the basics is the first step to building complex AI solutions. #Python #AI #MachineLearning #DataScience #30DayChallenge #M4ACE

Today, I focused on working with NumPy arrays. Building a solid foundation for data manipulation and analysis. Here’s what I practiced: 🔹 Created a 1D array with values from 1 to 15 🔹 Built a 2D array (3×4) filled with ones 🔹 Generated a 3×3 identity matrix 🔹 Explored key array properties like shape, type, and dimensions 🔹 Converted a regular Python list into a NumPy array This session helped me better understand how data is structured and handled in numerical computing. Getting comfortable with arrays is definitely a crucial step toward more advanced data analysis and machine learning tasks. Looking forward to building on this momentum 💡 #AI #MachineLearning #Python #NumPy #DataAnalysis #M4ACE

🌸 Iris Model Explained | OASIS Task 🌸 In this video, I break down the complete workflow of iris_model.py — from understanding the dataset to building and evaluating the model. 📊✨ 🔍 Key highlights: • Data loading and exploration • Preprocessing steps • Model building and training • Performance evaluation This explanation simplifies how machine learning models work using the classic Iris dataset 🌿 #MachineLearning #Python #DataScience #OASISInfobyte #IrisDataset #EDA #ModelBuilding

#PrincipalComponentAnalysis (PCA) is more than just a technique for dimensionality reduction - it’s one of the most powerful applications of eigenanalysis in data science. By identifying the directions of maximum variance, PCA simplifies complex datasets while preserving their essential structure. What’s inside this guide: * The math: Covariance matrices and Eigen-decomposition.  * The logic: From data centering to explained variance.  * The code: Python realizations using NumPy and scikit-learn. Swipe through the carousel below to explore the mechanics of PCA! The link to the full #Medium article with complete code is in the first comment. #DataScience #MachineLearning #Python #LinearAlgebra #AI #STEM

Day 72. Spent time going deeper into XGBoost today. Covered classification and worked through the math: gradients & hessian leaf weights similarity score & gain Some questions I tried to answer while learning: Why do we need Taylor expansion here? Why can’t we directly differentiate the objective? What makes decision trees non-smooth / non-differentiable? The key realization: since trees produce piecewise constant outputs, the loss surface isn’t smooth — which is why second-order approximation becomes necessary. Still revising, but things are starting to connect. Notes: https://lnkd.in/gCqHUeK9 #MachineLearning #XGBoost #LearningInPublic #Python #DataScience

Probability, linear algebra, calculus, matrices, Python, machine learning… all these things slowly coming together as I learn quantitative finance. Built and tested in Jupyter, here are 3 models I’ve been exploring lately: – Hidden Markov Model – Hierarchical Risk Parity – Sequential Monte Carlo Exploring more every day.

In my latest video, I break down the math behind logistic regression, derive the gradient descent update rules, explore vectorized implementations, and finally, code it from scratch in Python. Perfect for anyone preparing for ML interviews or looking to strengthen their foundations in machine learning. Video Link: youtu.be/cT_U40djaww   Channel Link: youtube.com/@datatrek

🔗 GitHub Repository: [https://lnkd.in/gXa9zEBs] Strengthening Machine Learning concepts with Logistic Regression Covered practical implementation of: ✔ Binary Classification (Single & Multiple Inputs) ✔ Polynomial Logistic Regression ✔ Multiclass Classification (OVR & Multinomial) ✔ Decision Boundaries & Model Evaluation using Python and scikit-learn Understanding how logistic regression predicts probabilities and solves classification problems gives deeper insight into real-world ML applications. From theory to implementation, every project adds more clarity and confidence to the learning journey. #MachineLearning #LogisticRegression #Python #DataScience #ScikitLearn #GitHub