In my latest video, I break down the math behind logistic regression, derive the gradient descent update rules, explore vectorized implementations, and finally, code it from scratch in Python. Perfect for anyone preparing for ML interviews or looking to strengthen their foundations in machine learning. Video Link: youtu.be/cT_U40djaww   Channel Link: youtube.com/@datatrek

Today was one of those days where things actually started making more sense in Binary Trees 🌳 I spent some time revising the basics: • height of tree • sum of nodes • count of nodes Then moved to a couple of important problems: • identical trees • subtree of another tree What clicked for me today is how recursion is not about memorizing code — it’s about trusting the pattern: go left → go right → combine the answer. Still not 100% perfect, but definitely more clarity than yesterday. Next target: LCA and diameter 👀 #DSA #BinaryTrees #LearningJourney #Python

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

🎬 Built a Movie Recommendation System using Python & ML Using content-based filtering to recommend movies based on similarity — here's a quick breakdown how it works: 1. TF-IDF Vectorizer converts movie descriptions into vectors. 2. Cosine Similarity measures how similar two movies are. 3. Random Forest classifier validates the results. Results: ◈ 16 movies ◈ 4 genres ◈ 97% model accuracy ◈ Toy Story & Finding Nemo topped similarity at 0.61 ◈ The Godfather & Goodfellas closely matched at 0.58 A great way to understand how Netflix-style recommendations work under the hood. Open to feedback and questions! 👇 #MachineLearning #Python #DataScience #RecommendationSystem #BuildInPublic #dataanalytics #datascience #mlproject #datanalyst #datascientist #scikitlearn

DSA Tip: Binary Trees (Part 2) Building a tree is one thing… traversing it is where the real power comes in. In Binary Trees, we don’t just store data, we visit nodes in a specific order. The 3 main ways: Inorder (Left -> Root -> Right) Preorder (Root -> Left -> Right) Postorder (Left -> Right -> Root) Each traversal gives a different perspective of the same data. Insight: How you traverse data can be just as important as how you store it. Quick Challenge: Given this tree: 5 / \ 3 7 What is the Inorder traversal? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #BinaryTree #Python #CodingTips #LearnToCode

Here's what's happening... Output: l 🐍 Did you get it right? The key here is chained indexing — name[0] gives you the string, then [1] digs into that string. Step by step: name[0] → "Ali" (first element of the tuple) "Ali"[1] → "l" (second character of the string, index starts at 0!) ✅ This is called chained indexing. Follow for more Python puzzles 🔥 #python #pythontips #learnpython #pythonprogramming #viral

💡 Solved Group Anagrams today! 🧠 Key idea: Instead of comparing strings, I used a frequency array (size 26) to create a unique key for each word. ⚠️ Catch in constraints: All characters are lowercase English letters, which allows us to use a fixed-size array of 26 for efficient hashing. 🚀 Result: Achieved O(n · k) time complexity. Great example of converting a comparison problem into a hashing problem! #LeetCode #DSA #Algorithms #Python #CodingInterview

🎬 Building my first Movie Recommendation System. Explored a dataset with 1.4M+ rows and found heavy missing values in key features like genres, keywords, and overview. My approach: dropping missing data to keep recommendations meaningful. What would you do? Drop or fill? Let me know your thoughts in the comments 👇 #MachineLearning #Python #DataScience

Need to sanity-check a CSV in seconds? Load it with pd.read_csv, then immediately inspect a few rows, the shape, column names, and data types. This quick pattern helps you catch wrong delimiters, missing headers, and type issues before you start analysis.#pandas #python #datascience #dataanalysis

Day7 of #30DayChartChallenge Theme: Multiscale Category: Distributions Tool: Python Data Source: python scikit-learn Datasets I worked with a few features from a machine learning dataset and plotted their distributions. At first, everything sits on different ranges. One stretches far, another stays tight, another somewhere in between. It looks fine, but comparing them like that is off. After scaling, they fall into the same range. Now the comparison actually makes sense. It’s a small step in most workflows, but seeing it visually makes the difference clearer. #30DayChartChallenge #python #Dataviz #Datascience