Niaz Chowdhury, PhD’s Post

Python Clarity Series – Episode 25 Topic: Mutable vs Immutable Function Behavior 📌 Why did my list change after function call? def modify(lst): lst.append(100) a = [1, 2] modify(a) print(a) Output: [1, 2, 100] 👉 Lists are mutable → changes reflect outside Now: def modify(x): x = x + 10 a = 5 modify(a) print(a) Output: 5 👉 Integers are immutable → no change outside 💡 Rule: Mutable → changes persist Immutable → changes don’t This confusion causes logic errors. #PythonBasics #FunctionConcepts #StudentClarity #python #clarity

Python Series — Day 3 🧠 Let’s level it up a bit 👇 What will be the output of this code? def modify_list(lst): lst.append(4) a = [1, 2, 3] modify_list(a) print(a) Options: A. [1, 2, 3] B. [1, 2, 3, 4] C. Error D. None Think carefully 👀 (Hint: It’s not about functions… it’s about how Python handles data) Drop your answer 👇 Answer tomorrow 🚀 #Python #CodingChallenge #LearningInPublic #DataEngineering #Tech

🐍 Day 116 — Cross Validation Day 116 of #python365ai 🔁 Cross-validation splits data multiple times. Example: from sklearn.model_selection import cross_val_score 📌 Why this matters: Provides more reliable performance estimates. 📘 Practice task: Run cross-validation on a model. #python365ai #CrossValidation #MachineLearning #Python

🎥 Project Explanation Video Here is my explanation for Iris Flower Classification project using Machine Learning. 🔗 GitHub Link: https://lnkd.in/gKwJNFrr #DataScience #MachineLearning #Python #CodeAlpha

📊 Day 12 of #100DaysOfBusinessAnalytics Today I explored correlation analysis using a heatmap in Python. Instead of analyzing variables individually, I looked at how they relate to each other. 📌 Key insights: • Most variables show weak to moderate relationships • Few variables have very low or no correlation • Helps identify which factors move together 👉 This is useful for understanding patterns and making better data-driven decisions. 💡 Key learning: Correlation helps in identifying relationships, but it does not imply causation. #100DaysOfBusinessAnalytics #BusinessAnalytics #DataAnalytics #Python #Pandas #Seaborn

Day 37 / #120DaysOfCode – LeetCode Challenge ✅ Problem Solved: • Search a 2D Matrix 💻 Language: Python 📚 Key Learnings: • Applied Binary Search on a 2D matrix • Learned how to treat matrix as a flattened sorted array • Practiced converting 1D index → 2D index (row, col) • Improved understanding of search space reduction • Strengthened logarithmic time complexity (O(log n)) thinking Better logic → Faster execution 🚀 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #BinarySearch #Algorithms #CodingJourney #Consistency #120DaysOfCode

Built Linear Regression from scratch using Python (no libraries) Wanted to understand what’s happening under the hood before moving to sklearn. So I implemented a simple model to predict marks based on hours studied using Gradient Descent. 🔹 What I did: Implemented the prediction function (y = wx + b) Calculated Mean Squared Error (MSE) manually Computed gradients and updated parameters over 1000 epochs 🔹 What I learned: How gradient descent updates weights step by step Why learning rate plays a critical role How loss decreases as the model learns 🔹 Result: The model successfully learned the relationship. Example: If a student studies 9 hours → predicted marks ≈ 89.3 🔗 Code: https://lnkd.in/gPHCenhB Next step: implementing this using NumPy and then sklearn. #MachineLearning #Python #LearningInPublic

ops-code just hit 50 downloads on the VS Code marketplace—that is pretty cool! Version 0.1.0 introduces tools: users can create their own python scripts that will run automatically after analysis, consuming the fem-results and producing tabular output displayed in the viewer. See the demo at https://lnkd.in/euRXSNE2 #OpenSees #StructuralEngineering #VSCode #Python #AI #3DVisualization #CivilEngineering

Start strong: XGBoost 3.2.1 delivers further speed improvements and categorical handling updates for predictive modeling. Changes: https://lnkd.in/gK4A79-H In ML work, these boost efficiency on larger datasets. Following XGBoost patches? Views? #XGBoost #MachineLearning #Python #DataScience #AIProgress

🐍 Day 103 — Decision Trees (Implementation) Day 103 of #python365ai 🧑💻 Example: from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() model.fit(X, y) 📌 Why this matters: Decision Trees handle both classification and regression tasks. 📘 Practice task: Train a simple decision tree model. #python365ai #DecisionTree #MachineLearning #Python