Niaz Chowdhury, PhD’s Post

🐍 Day 93 — Linear Regression (Implementation) Day 93 of #python365ai 🧑💻 Example: from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) 📌 Why this matters: This is your first real ML model. 📘 Practice task: Fit a simple regression model. #python365ai #MLModel #Python #DataScience

Claude just diagnosed me with a classic developer bug 😂 After hours of learning Python — functions, loops, dictionaries, if/else, and AI agent architecture — I started asking the same questions twice. Claude's response? ``` while awake == True:   ask_questions()       if questions == repeat:     print("Go to sleep Anil! 😄")     break ``` Turns out even humans need a break statement. 😄 The grind is real. But so is the progress. 💪 #Python #AI #MachineLearning #CareerChange #AIAgent #LearningToCode #Claude #100DaysOfCode

🐍 Day 92 — Linear Regression (Concept) Day 92 of #python365ai 📈 Linear regression models relationships between variables. Equation: y = mx + c 📌 Why this matters: It’s one of the simplest and most important ML models. 📘 Practice task: Think of predicting salary based on experience. #python365ai #LinearRegression #MachineLearning #Python

🔗 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

We built a Spam Email Classifier as a group using Machine Learning in Python. What it does: Detects whether an email is spam or not. Dataset: 10,000 emails 🤖 Model: Random Forest Classifier Accuracy: 88.7% | F1-Score: 86% Using a dataset from kaggle https://lnkd.in/dNZfH4Fr Tools used: Python · Scikit-learn · Pandas · Matplotlib It is now on my github https://lnkd.in/drKeE_se #MachineLearning #Python #AI #DataScience #StudentProject

I built a tiny Python library for AI agents. It's called ExAgent. No complex setup. No heavy framework. Just agents + skills. This video shows how it works in under a minute. Trying to make agent building as simple as writing a script. Feedback welcome 👇 #python #ai #opensource

Sorting lists of dictionaries or objects in Python often means writing small, repetitive lambda functions. There's a cleaner, faster way to grab specific items for sorting or processing. This little trick makes your data operations much more elegant and performant ✨. Do you use `itemgetter` or stick with `lambda` for sorting? Share your preferred method below! #Python #MachineLearning #AI #CodingTips #PythonTips

Completed learning Regularization in Machine Learning ✅ Understood how: 👉 Overfitting affects model performance 👉 Regularize High coefficient to Low coefficient l2- Regression| l2 Regularization 👉 Regularize High coefficient to zero - l1 👉 Lasso (L1) helps in feature selection 👉 Ridge (L2) helps in reducing model complexity Practiced implementing these concepts using Python. Step by step improving my ML skills 💻📈 #MachineLearning #Python #DataScience

Learn deep learning with Python and TensorFlow, including basics, benefits, and real-world applications, with this comprehensive tutorial and guide https://lnkd.in/gz4WZgck #DeepLearningWithPython Read the full article https://lnkd.in/gz4WZgck

Day 7 - Hash Table Deep Dive The answer is O(1) AMORTIZED - and the 'amortized' part is what trips people up. In the best case, hash lookups are O(1). But with hash collisions, worst case is O(n). The key insight: with a good hash function and load factor below 0.75, the AVERAGE case stays O(1). Python dicts use open addressing with random probing, keeping collisions rare. This is why interviewers ask 'average' vs 'worst case' - they want to see if you understand the nuance. Drop your answer! Heart for correct ones. Follow DatascienceBro for Week 2! #datastructures #hashtable #timecomplexity #python #codinginterview #algorithms #bigO #programming #techinterview #softwareengineering