Niaz Chowdhury, PhD’s Post

🚀 Day 38 – Machine Learning Journey Cross Validation (Python Implementation) Today I implemented Cross Validation in Python to evaluate models more reliably instead of trusting a single train–test split. 🔹 Why in code Measures consistency, not luck Reduces overfitting bias Gives a stable performance estimate 🐍 Minimal Python example (scikit-learn) from sklearn.model_selection import KFold, cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() kf = KFold(n_splits=5, shuffle=True, random_state=42) scores = cross_val_score(model, X, y, cv=kf) print(scores.mean()) 🔹 What this does: Splits data into 5 folds Trains on 4, validates on 1 Repeats for all folds Reports the average score 🔑 Key takeaway: > Cross validation doesn’t change the model — it validates whether the model can be trusted. #Day38 #MachineLearning #CrossValidation #Python #ScikitLearn #LearningJourney #ModelEvaluation #DataScience #AI #LearningJourney

Explored the Naive Bayes classification algorithm using Python and scikit-learn. Worked with a structured dataset, performed label encoding, train-test split, and trained a Gaussian Naive Bayes model to analyze classification performance on both training and testing data. This hands-on implementation helped me better understand how probabilistic models work in real-world machine learning workflows. #MachineLearning  #NaiveBayes  #GaussianNB   #DataScience  #Python  #ScikitLearn  #MLPractice   #LearningByDoing  #JupyterNotebook  #AIStudent

Implemented Linear Regression from scratch using NumPy to strengthen my grasp of: • Feature scaling & normalization • Gradient descent optimization • Loss minimization • Model training vs inference Instead of relying on libraries, I focused on understanding how each component works under the hood — the kind of foundation that scales when moving to more complex models. This project reflects my transition from using machine learning tools to understanding them. Actively building, breaking, and improving. . . . #MachineLearning #DataScience #Python #NumPy #LearningJourney #StudentToProfessional #MLFoundations #CareerGrowth

Task 3 completed!! 🚗 Car Price Prediction using Machine Learning Built an ML model to predict car selling prices using Python and Random Forest, achieving an R² score of ~0.96. A great hands-on experience with EDA, preprocessing, and model evaluation. GitHub= https://lnkd.in/gFS-Hs7c  #Python #DataAnalysis #OasisInfobyte #DataScienceStudent #MachineLearning #DataScience #Python #AI #StudentProject

Built a regression model to predict movie ratings using features like genre, director, and actors. This project helped me understand data preprocessing, feature engineering, and regression techniques in Machine Learning. #MachineLearning #Python #DataScience #Regression #ScikitLearn #MLProject #Codesoft

🚀 Task 1 Completed: House Price Prediction using Machine Learning As part of my Machine Learning learning journey, I worked on a House Price Prediction model using Python. 🏠 Project Overview: The goal was to predict house prices based on key features such as: Square Feet Area Number of Bedrooms 🛠 Tech Stack Used: Python Pandas Scikit-learn Linear Regression 📌 What I learned: Working with real-world CSV datasets Feature selection for regression problems Training and using a Linear Regression model Taking user input and making predictions This task helped me understand the end-to-end machine learning workflow, from data loading to prediction. 📂 Project available on GitHub (README included) https://lnkd.in/gf2kq6rk #MachineLearning #Python #LinearRegression #AI #DataScience #LearningByDoing #StudentDeveloper

Day 22 & 23 | AI/ML Learning Journey | Python —Pandas Topic: Pandas (Practice) Over the last two days, I focused on Pandas fundamentals by working with real datasets. What I covered: •DataFrame methods — head(),tail(),info(), describe() etc. •Loading datasets from Kaggle •Data selection — iloc(position) , loc(label) •Filtering & Query filter •Data cleaning techniques  • Handling missing values  • Removing duplicates  • Converting data types Consistency Challenges. #AIML #DataScience #Pandas #Python #MachineLearning #LearningJourney #Kaggle #DataCleaning

🐍 Python Quiz: Do you actually know how yield works? Take a look at the code snippet below. Without running it in your IDE or asking an AI, what is the exact output? def countdown(n):   while n > 0:     yield n     n -= 1 for x in countdown(3):   print(x, end=' ') The Rules: 🚫 No Google. 🚫 No AI. 🧠 Use your brain. The Real Challenge: Don't just post the answer—explain HOW the answer came to be in the comments. Specifically, where did the next() call go? #Python #CodingChallenge #SoftwareEngineering #PythonProgramming

Built a simple end-to-end Machine Learning pipeline using Logistic Regression to predict student pass/fail outcomes based on study hours. This project helped me understand: – Feature engineering and target creation – Train–test splitting – Binary classification with Logistic Regression – Model evaluation using accuracy and confusion matrix Tech stack: Python, NumPy, Pandas, Matplotlib, Scikit-Learn Currently focusing on strengthening ML fundamentals before moving to more complex models. #machinelearning #python

Hello Everyone, My First Video in the Python + AI Series is Live [AI PDF Summarizer Using Python]! AI is everywhere — but most people think it’s too complex or requires heavy ML & math. So I started a Python AI Series where I focus on: ✅ Practical use cases ✅ Clean Python code ✅ Real-world automation ✅ Beginner-friendly explanations 🎥 In my first video, I show how to: 👉 Build an AI-powered PDF Summarizer using Python 👉 Understand how AI models work in the background 👉 Control cost, performance, and architecture 👉 Use AI without machine learning or data science This series is for: 1. Python beginners 2. Automation engineers 3. Students & working professionals Anyone curious about AI but unsure where to start 📌 This is just the beginning — next videos will be more exciting ! 🔗 Watch the video here: https://lnkd.in/dBiSsADm If you’re learning Python or planning to move into AI — this series is for you. #Python #ArtificialIntelligence #PythonAI #Automation #AIProjects #LearningByBuilding #TechContent #DeveloperJourney