Geo AI Tutorial for Industrial Engineering with Python

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

I’ve been spending time lately diving deeper into NumPy to master efficient data manipulation. From understanding N-dimensional arrays to implementing linear algebra operations like matrix inversion and eigenvalues, it's fascinating to see how these fundamentals power the most complex Machine Learning models. Current focus: Optimizing array slicing and indexing. Exploring data preprocessing and synthetic dataset generation. Bridging the gap between mathematical theory and Python implementation. Onwards and upwards! 🚀 #DataScience #Python #NumPy #MachineLearning #ContinuousLearning #WebDevelopment

Day 14 of my AI & Data Science Journey Today, I learned about functions in Python, with a focus on implementing nested functions. What I explored: Concept and components of functions Types and classification of functions Implementation of user-defined functions Key focus: Nested functions (function inside another function) How inner functions can access variables from the outer function Practical implementation of nested functions to organize code better Practiced writing programs using nested functions to break down problems into smaller parts. ✨ Key Insight: Nested functions help improve code structure, readability, and reusability by organizing logic within a function. They are useful when a function is needed only within another function. #Python #Programming #AI #DataScience #LearningJourney #Coding #Functions #Consistency

🚗 Car Price Prediction using Machine Learning Happy to share my ML project where I built a model to predict car prices based on various features. 🔧 Technologies Used: - Python - Scikit-learn - Pandas, NumPy 📌 Key Features: ✔ Data preprocessing ✔ Model training & evaluation ✔ Prediction system 🔗 GitHub Repository: https://lnkd.in/g_yrducF 🎥 Project Demo: [Paste your video link here] #MachineLearning #Python #DataScience #CodeAlpha #AI

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

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

How can you evaluate an AI model's robustness before real-world failures occur? In this webinar, we’ll demonstrate how to use the open source Natural Robustness Toolkit (NRTK) to create reproducible workflows for testing model performance. You’ll learn how to: ✅ Install and configure NRTK in Python ✅ Apply perturbations to expand existing datasets ✅ Design parameter sweeps to measure performance degradation ✅ Evaluate models under simulated operational conditions 📅 April 15, 2026 | 12–1 PM 👉 Register here: https://ow.ly/Ncnr50YBmK7 #AIResearch #MachineLearning #ModelValidation #NRTK #Python

Week 1 – Learning Progress in Generative AI 🚀 This week I focused on: Python fundamentals for data handling Working with libraries like pandas, numpy and matplotlib Setting up the development environment in VS Code Key takeaway: Understanding the environment setup and libraries is just as important as writing code. Small setup issues can slow you down, but solving them builds confidence. Looking forward to diving deeper into real-world data problems next. #GenerativeAI #Python #LearningJourney #CareerTransition

🌸 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

Day 17 of my AI & Data Science Journey Today, I learned about the scope of variables in Python and how they behave in different parts of a program. What I explored: Concept of variable scope Local variables (defined inside a function) Global variables (defined outside functions) Use of the global keyword Understood how variables can be accessed and modified depending on their scope. ✨ Key Insight: Knowing the scope of variables helps avoid errors and makes programs more organized and efficient. #Python #Programming #AI #DataScience #LearningJourney #Coding #Consistency