NumPy Indexing and Slicing Techniques

Day 35 at Luminar Technolab Took NumPy a step further worked on reshaping arrays and applying indexing,slicing more practically. Starting to connect the concepts instead of just learning them #Python #NumPy #LearningJourney

From concepts → to code 💻 Explored Regularization (L1 & L2) with hands-on implementation and performance comparison. Analyzed how coefficients change, reduced overfitting, and improved model generalization. Gained deeper insight into the bias-variance tradeoff through practical learning. #ML #DataScience #LearningJourney #Python

Day 39 at Luminar Technolab Focused on data manipulation in Pandas adding columns, sorting values, and filtering using conditions. Also explored useful methods like head() and tail() for quick data insights. #Python #Pandas #DataAnalysis #LearningJourney #Consistency

🔁 Exploring Sorting Algorithms in Python Today I practiced two fundamental sorting techniques: ✅ Bubble Sort ✅ Selection Sort 💡 Key Learnings: * Bubble Sort repeatedly swaps adjacent elements to push larger elements to the end * Selection Sort selects the minimum element and places it in the correct position * Understanding time complexity becomes clearer when you count operations manually #Python #DataStructures #Algorithms #CodingJourney #100DaysOfCode #LearningInPublic

The Pearson correlation coefficient, is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables, ranging from -1 to +1. A value of +1 implies a perfect positive linear relationship, -1 a perfect negative relationship, and 0 no linear correlation. The following video shows how to setup envoirnment for video (.mp4) or animation of the coefficient using python script and Jupyter Notebook. #Python #Machinelearning #Scripting

"Fisher-Pearson Moment based Coefficient of Skewness" self made statistical function in python. Types of Skewness are Negatively Skewed, Symmectric (Not Skewed), Positively Skewed. #python #DataScience #statistics #skewness #fisher #pearson #moment #sample #population #coefficient #data #distribution #negative #positive #bias #symmectric #gamma

Day 24/100 – DSA Journey Problem: Intersection of Two Arrays Initially thought of using loops, but that would increase time complexity. Then realized the problem requires only unique common elements, which makes sets a perfect fit. Converted both arrays into sets and used intersection to directly get the result in an efficient way. Key Learning: Choosing the right data structure can simplify the entire problem. #Day24 #100DaysOfCode #DSA #Python #LeetCode #ProblemSolving

🎥 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 32 at Luminar Technolab Focused on file handling in Python, reading data from files, processing it, and extracting useful insights. A small step closer to handling real world data. #Python #FileHandling #LearningJourney #ContinuousGrowth

Longest Consecutive Sequence: O(n) via Sequence Start Detection Sorting achieves O(n log n). HashSet enables O(n) with key insight — only start sequences from their true beginning. If n-1 exists, skip n (it's part of another sequence). This prevents redundant work, ensuring each number processed once. Start Detection Optimization: The n-1 check is what makes this O(n) not O(n²). Each element visited at most twice: once in outer loop, once during sequence counting. Recognizing when to skip prevents nested iteration. Time: O(n) | Space: O(n) #HashSet #SequenceDetection #LinearTime #OptimizationTrick #Python #AlgorithmDesign #SoftwareEngineering