Python Algorithm Practice Repository

Not every day is about solving problems, some days are about understanding concepts. Day 38/100 — Data Structures & Algorithms Journey Today I focused on learning the Sliding Window technique instead of solving problems. Taking time to understand the pattern deeply before jumping into implementation. Today’s Focus: Understanding how sliding window works Learning when to expand and shrink the window Studying problem patterns where it applies Building intuition step by step Why this matters? Because strong concepts make problem-solving faster and more efficient. Key Takeaways: Learning is also progress Clarity builds confidence Patterns simplify complex problems Consistency matters more than intensity Taking it slow, but moving forward #Day38 #DSA #LeetCode #ProblemSolving #CodingJourney #100DaysOfCode #SoftwareEngineering #Python #InterviewPreparation #LearnInPublic #Consistency

Day 3 Mastering the logic behind the code. 💻 Today’s deep dive: Booleans and Logical Operators. It’s fascinating to see how complex machine decisions are actually just a series of simple True or False evaluations. I’ve been exploring the Boolean data type and how comparison operations drive decision-making in software. It’s not just about 'running code'; it's about structuring logic that scales. Progress over perfection. 📈 Moving through the 'Lesson Takeaways' today. There is something so satisfying about seeing a complex scenario broken down into a simple flowchart. What are you currently learning? Let's connect! #BuildInPublic #TechStack #CareerGrowth #ComputerScience #PythonProgramming #TechEducation #Python #LearningToCode #ContinuousImprovement

Day 3 Mastering the logic behind the code. 💻 Today’s deep dive: Booleans and Logical Operators. It’s fascinating to see how complex machine decisions are actually just a series of simple True or False evaluations. I’ve been exploring the Boolean data type and how comparison operations drive decision-making in software. It’s not just about 'running code' it's about structuring logic that scales. Progress over perfection. 📈 Moving through the 'Lesson Takeaways' today. There is something so satisfying about seeing a complex scenario broken down into a simple flowchart. What are you currently learning? Let's connect! #BuildInPublic #TechStack #CareerGrowth #ComputerScience #PythonProgramming #TechEducation #Python #LearningToCode #ContinuousImprovement

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

I’ve always found that the best way to truly understand Data Structures is to visualize them. 🧠 I recently put together a complete, fully-commented implementation of a Singly Linked List in Python. To make it easier to grasp, I included memory diagrams right alongside the code to show exactly what's happening under the hood when we insert, delete, or search for nodes. I've attached the complete reference as a PDF. Feel free to download it, save it for your interview prep, or share it with anyone learning DSA! What data structure should I tackle and visualize next? 👇 #DataStructures #Algorithms #Python #Coding #SoftwareEngineering #TechCareers #LearningToCode

🚀 Day 04 of My Machine Learning Journey: NumPy Data Types (dtypes) Today, I learned about NumPy data types (dtypes), which define the type of elements stored in an array. I explored: ✅ Different types like int, float, and bool ✅ How NumPy uses fixed data types for better performance ✅ Why choosing the right dtype helps optimize memory usage Understanding dtypes helps write more efficient and faster code — an important step for Machine Learning. 💡 #MachineLearning #NumPy #Python #LearningJourney #Day04

🚗 Built an Automatic Number Plate Recognition (ANPR) System! Excited to share my recent project where I developed a real-time ANPR system using YOLOv8 and EasyOCR. 🔍 Key Features: • Real-time number plate detection • OCR-based text extraction • Custom dataset creation This project helped me strengthen my skills in Computer Vision and Machine Learning. 🔗 GitHub Repository: https://lnkd.in/g_5r5wcW #MachineLearning #ComputerVision #YOLO #Python #DataScience

🚀 Python Series – Day 12: String Methods Text processing ka next level hai — String Methods. Aaj humne seekha: 👉 How to manipulate and clean text efficiently 📌 Key Highlights: ✔ Text transformation (upper/lower) ✔ Data cleaning (strip, replace) ✔ Splitting & joining strings 📌 Practical Use Cases: User input validation Data cleaning Text formatting 💡 Practice Task: Apply multiple string methods Analyze output Build small text-processing logic 📈 Strong fundamentals = better real-world coding 🔔 Follow Logic Gurukul for daily Python learning 💬 Comment "DAY12" for complete roadmap #Python #Programming #DataScience #AI #MachineLearning #Coding #LearnPython #TechSkills #CareerGrowth #LogicGurukul

🚀 Matrix Multiplication: Code Implementation (Data Structures And Algorithms) This Python code illustrates how to perform matrix multiplication. The function takes two matrices as input and returns their product. It ensures that the matrices are compatible for multiplication (number of columns in the first matrix equals the number of rows in the second). The algorithm iterates through the rows of the first matrix and the columns of the second matrix to compute each element of the resulting matrix. Understanding the nested loops and the dot product calculation is key to understanding matrix multiplication. #Algorithms #DataStructures #CodingInterview #ProblemSolving #professional #career #development

📊 Day 6 | K-Nearest Neighbors (KNN) 🤝📍 Today, I learned about K-Nearest Neighbors (KNN), a simple and intuitive Machine Learning algorithm. KNN works on the idea of distance — it classifies a data point based on the majority class of its nearest neighbors. 📌 In simple terms: “Similar data points are close to each other.” Example: ✔ Recommending products ✔ Classifying customers To understand this, I implemented KNN using Python and observed how it predicts based on nearby data points 💻 KNN is simple but powerful for many classification problems. #MachineLearning #KNN #DataScience #LearningInPublic #Python