Efficiently Merging Sorted Arrays with Two-Pointer Technique

Sometimes, solving a problem is about managing a window efficiently. Day 17/100 — Data Structures & Algorithms Journey Today’s Problem: Longest Substring Without Repeating Characters Approach: I used the sliding window technique to maintain a substring without duplicate characters. As I expanded the window, I checked for duplicates. If a duplicate was found, I shrank the window from the left until all characters were unique again. This allowed me to efficiently track the maximum length of a valid substring. Key Takeaways: - Sliding window helps optimize substring problems - Managing duplicates efficiently is crucial - Expanding and shrinking dynamically improves performance This problem strengthened my understanding of window-based algorithms. #DSA #LeetCode #SlidingWindow #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #JobReady #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Sometimes, organizing the data first makes the solution much simpler. Day 18/100 — Data Structures & Algorithms Journey Today’s Problem: Merge Intervals Approach: I started by sorting the intervals based on their starting values. Then, I iterated through the list and compared each interval with the last merged interval. If there was an overlap, I merged them by updating the end value. Otherwise, I added it as a new interval. Key Takeaways: - Sorting simplifies interval-based problems - Greedy approach helps in making optimal local decisions - Understanding overlap conditions is key This problem helped me understand how combining sorting and greedy logic leads to efficient solutions. #DSA #LeetCode #Intervals #Greedy #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #JobReady #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Some problems don’t have one answer — they require exploring all possibilities. Day 19/100 — Data Structures & Algorithms Journey Today’s Problem: Different Ways to Add Parentheses This problem introduced me to a powerful concept — Divide and Conquer using recursion. Approach: Instead of evaluating the expression directly, I split the expression at every operator. For each split, I recursively solved the left and right parts and then combined the results. This allowed me to generate all possible outcomes based on different ways of placing parentheses. Key Takeaways: - Recursion helps explore multiple possibilities - Divide and Conquer simplifies complex expressions - Breaking problems into smaller parts makes them easier to solve This problem improved my understanding of recursive thinking and expression evaluation. #DSA #LeetCode #Recursion #DivideAndConquer #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #JobReady #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Understanding how things work internally is more important than relying on built-in functions. Day 16/100 — Data Structures & Algorithms Journey Today’s Problem: Sort an Array Instead of using built-in sorting methods, I implemented Merge Sort to understand the logic behind efficient sorting. Approach: I used the divide and conquer technique: - Divide the array into smaller parts - Sort each part recursively - Merge the sorted parts This approach ensures O(n log n) time complexity and helps build a strong understanding of sorting algorithms. Key Takeaways: - Merge Sort is a fundamental algorithm for efficient sorting - Breaking problems into smaller subproblems simplifies complexity - Understanding internal logic is crucial for interviews Focusing on building strong fundamentals step by step. #DSA #LeetCode #Sorting #MergeSort #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #JobReady #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Learn the fundamentals of NumPy in Python with this beginner-friendly introduction! 🚀 In this video, I’ve covered: What is NumPy? Why NumPy is important NumPy arrays basics Difference between lists and arrays Basic operations in NumPy NumPy is one of the most powerful libraries in Python for numerical computing and is widely used in Data Science, Machine Learning, and AI. See the Details Video here : https://lnkd.in/d4ShsbXj 💡 If you are starting your journey in Python or AI, this video will help you build a strong foundation. #NumPy #Python #PythonForBeginners #LearnPython #DataScience #MachineLearning #AI #Coding #Programming #PythonTutorial #Developers #Tech #ArtificialIntelligence #DataAnalysis

📊 Linear Regression from Scratch — OLS + SGD I built Linear Regression from scratch in Python — no sklearn, only math and numpy. This wasn’t just coding. I went step by step: • Derived OLS using matrix form • Implemented SGD from gradients • Verified results against sklearn Final result: R² ≈ 0.994 (matched sklearn closely) This project helped me understand what actually happens behind .fit() — not just use it blindly. 📖 Article: https://lnkd.in/grC8jMk2 💻 Code + Notes: https://lnkd.in/gPb6BTNv If media quality drops on LinkedIn, everything is available clearly in the repo. I’m currently building ML algorithms from scratch to strengthen fundamentals. Next: Logistic Regression. #MachineLearning #Python #DataScience #LinearRegression #MLFromScratch #OpenToWork

PYTHON SERIES GENERATORS 🔹 What are Generators? Generators are functions that return values one at a time using yield instead of returning all at once. 👉 In simple terms: Generate values on the fly, not all at once. 🔹 Why use Generators? ✔ Saves memory ✔ Works efficiently with large data ✔ Faster execution for big datasets. 🔹 Example: def count_up(n): for i in range(n): yield i for num in count_up(5): print(num) 🔹 Output: 0 1 2 3 4 🔹 Generator vs List: ✔ List → stores all values in memory ✔ Generator → produces values one by one. 🔹 Real-world example: Reading large files, streaming data, handling big datasets. 💡 Key Idea: Use generators when working with large data to improve performance #Python #Generators #Coding #Programming #LearnPython #Developer #SoftwareEngineering #100DaysOfCode #Tech

atomcamp AI bootcamp, Update: After an introduction to the basics of Python, we are ready to use Python libraries for data handling and analysis. We started off with NumPy which is the go-to library for handling numerical data. ndarray (N-dimensional array) is the core data structure in NumPy, providing a fast, memory-efficient, multi-dimensional container for homogeneous data (same type). It enables efficient numerical computing, allowing for element-wise operations, broadcasting, and flexible slicing/reshaping. We studied simple operations, such as addition, subtraction, scalar multiplication, element wise multiplication etc. that can be performed using ndarrays. Thank you Maimoona Khilji for the engaging and informative session. #Python #Numpy #Programming #DataScience #DataAnalysis #AI #SoftwareEngineering #TechInnovation #ContinuousLearning #Automation

Python remains the top programming language in 2026—powering AI, Data Science, Web Development, and more. 🚀 At STEAM MINDS, we're equipping aspiring innovators with this essential skill to unlock global career opportunities and drive real-world impact. #EdTech #TechSkills #Python #EdTech #STEAMEducation #Innovation #STEAMMinds

Day 14 of my AI & Data Science Journey Today, I learned about functions in Python, with a focus on implementing nested functions. What I explored: Concept and components of functions Types and classification of functions Implementation of user-defined functions Key focus: Nested functions (function inside another function) How inner functions can access variables from the outer function Practical implementation of nested functions to organize code better Practiced writing programs using nested functions to break down problems into smaller parts. ✨ Key Insight: Nested functions help improve code structure, readability, and reusability by organizing logic within a function. They are useful when a function is needed only within another function. #Python #Programming #AI #DataScience #LearningJourney #Coding #Functions #Consistency