Hash Table Time Complexity Explained

Day 37 / #120DaysOfCode – LeetCode Challenge ✅ Problem Solved: • Search a 2D Matrix 💻 Language: Python 📚 Key Learnings: • Applied Binary Search on a 2D matrix • Learned how to treat matrix as a flattened sorted array • Practiced converting 1D index → 2D index (row, col) • Improved understanding of search space reduction • Strengthened logarithmic time complexity (O(log n)) thinking Better logic → Faster execution 🚀 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #BinarySearch #Algorithms #CodingJourney #Consistency #120DaysOfCode

📊 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

🌸 Iris Model Explained | OASIS Task 🌸 In this video, I break down the complete workflow of iris_model.py — from understanding the dataset to building and evaluating the model. 📊✨ 🔍 Key highlights: • Data loading and exploration • Preprocessing steps • Model building and training • Performance evaluation This explanation simplifies how machine learning models work using the classic Iris dataset 🌿 #MachineLearning #Python #DataScience #OASISInfobyte #IrisDataset #EDA #ModelBuilding

🐍 Day 95 — Model Evaluation (Mean Squared Error) Day 95 of #python365ai 📏 Evaluate models using metrics like MSE. Example: from sklearn.metrics import mean_squared_error 📌 Why this matters: We need to measure how good a model is. 📘 Practice task: Compute error for predictions. #python365ai #ModelEvaluation #ML #Python

🚀 Generators: Memory-Efficient Iteration (Python) Generators are a special type of function that allows you to create iterators in a memory-efficient way. Instead of returning a list of values, generators yield values one at a time using the `yield` keyword. This is particularly useful when dealing with large datasets, as it avoids loading the entire dataset into memory. Generators can be implemented using either generator functions (using `yield`) or generator expressions (similar to list comprehensions but with parentheses). Generators are essential for optimizing memory usage and improving performance in data processing applications. #Python #PythonDev #DataScience #WebDev #professional #career #development

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

Python Series — Day 3 🧠 Let’s level it up a bit 👇 What will be the output of this code? def modify_list(lst): lst.append(4) a = [1, 2, 3] modify_list(a) print(a) Options: A. [1, 2, 3] B. [1, 2, 3, 4] C. Error D. None Think carefully 👀 (Hint: It’s not about functions… it’s about how Python handles data) Drop your answer 👇 Answer tomorrow 🚀 #Python #CodingChallenge #LearningInPublic #DataEngineering #Tech

I started solving algorithmic problems daily to improve how I think, not just how I code. So I built this repository: 22+ Python problems → from basic to challenging Focused on logic, patterns, and problem-solving Some examples: Array manipulation String processing Mathematical logic Pattern-based problems This is less about “solutions” and more about building thinking frameworks. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 𝗼𝗿 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀, 𝘁𝗵𝗶𝘀 𝗺𝗶𝗴𝗵𝘁 𝗵𝗲𝗹𝗽. I’m consistently adding more problems and solutions as part of my daily practice. If you want to follow along or use it as a resource, the repo is in the comments. #Python #Algorithms #Coding #DataStructures #Learning

🚀 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

🚀 Solved: Find a String (Substring Count) Challenge Just solved another problem on HackerRank under the Python Strings section! ✅ 🧠 Problem Overview: Count how many times a substring appears in a string — including overlapping occurrences. 🔍 Key Learnings: Practiced string traversal techniques Understood why built-in methods like count() may not always work (no overlapping support) Strengthened concepts of slicing and iteration in Python 💡 Example Insight: For string "ABCDCDC" and substring "CDC", the answer is 2 (overlapping counts matter!). ⚡ Approach Used: Iterated through the string Compared substrings using slicing Counted valid matches efficiently 📈 Problems like this help build strong fundamentals in string manipulation, which is crucial for coding interviews and real-world applications. #Python #HackerRank #Coding #Strings #ProblemSolving #DSA #LearningJourney #AI link of #Solution :- https://lnkd.in/gtqcy8fX