💻 Day 9: Advanced Sorting Today I learned and implemented the following sorting techniques: 🔹 Merge Sort • Divide and Conquer approach • Recursive implementation • Efficient for large datasets 🔹 Recursive Bubble Sort 🔹 Recursive Insertion Sort 🧠 Key takeaways: • Recursion simplifies complex logic into smaller subproblems • Merge Sort offers better time complexity compared to basic sorting algorithms • Understanding recursion deeply helps in mastering advanced algorithms #striversa2zdsasheet #problemsolving #leetcode #LearningInPublic #DSA #SortingAlgorithms #Recursion #Python

🚀 Built a House Price Prediction Model using Machine Learning In this project, I implemented: ✅ Linear Regression ✅ Ridge Regression ✅ Lasso Regression 📊 Compared model performance using RMSE & R² score 📉 Observed how regularization reduces overfitting Key Learning: Lasso helped in feature selection by shrinking some coefficients to zero. #MachineLearning #Python #DataScience #FinalYearProject

#Day36 of my second #100DaysOfCode I focused more on understanding model quality and reliability today. ML: • Learned about VIF (Variance Inflation Factor) and how it’s used to test multicollinearity between features • Understood why multicollinearity can negatively impact model interpretation • Studied model simplification techniques • Explored Bayesian Information Criterion (BIC) and how it helps balance model fit vs complexity Good day for strengthening statistical intuition behind ML models. #WomenWhoCode #MachineLearning #DataScience #ModelSelection #Python #LearningInPublic

Mastered NumPy for numerical computing. Comparing Python lists vs. NumPy arrays was eye-opening—vectorization isn't just a feature, it's a necessity for high-performance AI. ⚡ Special thanks to Elevate Labs for the structured challenges. It’s one thing to read about these concepts, but another to build them from scratch! #Elevate Labs,#Python #AIML #DataScience #SQLite #NumPy #Pandas #EngineeringStudent #BackendDevelopment #TechLearning

I’ve used both statsmodels and pingouin extensively in experimental analysis. They address different analytical needs. One is built around explicit model specification. The other is optimized for structured hypothesis testing. The distinction isn’t about syntax. It’s about analytical framing. Selecting a tool is straightforward. Selecting the appropriate statistical approach requires more care. #Statistics #DataAnalysis #Research #Python

Explored the Titanic dataset using a structured EDA approach—starting from data loading and profiling to univariate and bivariate analysis. Focused on data quality checks, feature engineering, and extracting meaningful insights before modeling. A great exercise in understanding how much story the data tells even before machine learning. Guided by Harshvardhan Singh #DataAnalytics #EDA #Python #DataProfiling #FeatureEngineering #LearningByDoing

Not all preprocessing is the same. Sometimes, the difference is mathematical. In this project, I focused on feature transformation specifically understanding when to scale and when to normalize. Using Python, I worked with real-world data to: • Apply Min-Max Scaling for distance-based algorithms • Use Box-Cox transformation to correct skewed distributions • Compare distribution behavior before and after transformation • Analyze how statistical assumptions influence model choice The objective wasn’t just transformation. It was understanding why certain models require specific data behavior. Scaling adjusts magnitude. Normalization adjusts distribution. Small preprocessing decisions can significantly influence model stability and interpretability. #DataScience #MachineLearning #RegressionAnalysis #Statistics #FeatureEngineering #Python

🚀 Excited to share my Machine Learning – Supervised Learning Algorithms repository! From Linear Regression to Naive Bayes, I’ve implemented key supervised learning algorithms with Python. Aimed at anyone looking to learn or explore ML practically. Check out the full code here: 👉 https://lnkd.in/gKyyN9E2 💡 Feedback and contributions are welcome! Let’s learn and grow together. #MachineLearning #Python #AI #ML #DataScience #SupervisedLearning #GitHub #OpenSource

Excited to announce the start of my machine learning blog! This will explore a range of ideas, from underlying theory to practical applications, highlighting concepts important for a modern machine learning researcher. First post: Building a multiprocessing DataLoader from scratch. I break down PyTorch's DataLoader class by building a simplified version, focusing on how Python's multiprocessing module enables parallel data loading whilst training the model. You'll see how multiprocessing queues coordinate between worker processes and the main training loop—and why this matters for your training pipeline. Using a toy dataset, I compare single-process vs. multiprocess loading, ultimately showing how even a simple implementation can lead to massive improvements in loading time (over 6 times faster!). Link to the blog: [https://lnkd.in/eg6abKWg] #pytorch #machinelearning #ML #deeplearning #python

Sometimes, the best way to understand how a machine works is by observing it in its simplest form. Last weekend, I spent some time building a tabular Q-Learning simulation from scratch using Python—without any heavy AI libraries—to observe how a digital entity learns to navigate its environment purely through trial, error, and a penalty system. One of the most interesting takeaways from this experiment wasn't the final result, but rather the process of watching the state-value heatmap form in real-time. It mathematically demonstrates that behaviors like risk aversion and route optimization do not need to be explicitly programmed. Instead, they emerge naturally when the machine is allowed to make wrong decisions, hit boundaries, and experience the penalties. I've documented a short observation on the value of letting machines make mistakes in my latest piece. (Link to the full article is in the first comment below 👇) #MachineLearning #ReinforcementLearning #DataScience #Python #DataAnalytics