Probability Calculations in Python on HackerRank

Claude just diagnosed me with a classic developer bug 😂 After hours of learning Python — functions, loops, dictionaries, if/else, and AI agent architecture — I started asking the same questions twice. Claude's response? ``` while awake == True:   ask_questions()       if questions == repeat:     print("Go to sleep Anil! 😄")     break ``` Turns out even humans need a break statement. 😄 The grind is real. But so is the progress. 💪 #Python #AI #MachineLearning #CareerChange #AIAgent #LearningToCode #Claude #100DaysOfCode

Just completed the Gradient Descent lab in Andrew Ng's ML Specialization — and it genuinely clicked for me here. The concept: instead of guessing the best values for w and b in a linear model, gradient descent finds them automatically by repeatedly moving in the direction that reduces error. What I built from scratch in Python: ✅ compute_cost() — measures how wrong the model is ✅ compute_gradient() — calculates which direction to move ✅ gradient_descent() — runs 10,000 iterations to find optimal parameters What surprised me most: → Starting from w=0, b=0, the algorithm found w≈200, b≈100 for a house price dataset → The cost dropped rapidly at first, then slowed as it approached the minimum — exactly like rolling a ball to the bottom of a bowl → Setting the learning rate too high (α = 0.8) caused the model to completely diverge — cost shot up instead of down That last point was the most valuable. Seeing divergence visually made the theory real. Building these functions line by line beats reading about them any day. #MachineLearning #Python #AndrewNg #LearningInPublic #DataScience #GradientDescent

Day 72. Spent time going deeper into XGBoost today. Covered classification and worked through the math: gradients & hessian leaf weights similarity score & gain Some questions I tried to answer while learning: Why do we need Taylor expansion here? Why can’t we directly differentiate the objective? What makes decision trees non-smooth / non-differentiable? The key realization: since trees produce piecewise constant outputs, the loss surface isn’t smooth — which is why second-order approximation becomes necessary. Still revising, but things are starting to connect. Notes: https://lnkd.in/gCqHUeK9 #MachineLearning #XGBoost #LearningInPublic #Python #DataScience

🎥 Here’s a quick demo of my Sentiment Analysis Web Application in action! This project predicts whether a given text is Positive, Negative, or Neutral using Machine Learning. 🔹 Built using Python, TF-IDF, and ML models 🔹 Integrated with a Flask web application 🔹 Deployed live using Render 👉 Try it here: https://lnkd.in/dVU2kzP8 I’ve also shared the project screenshots and code details in my previous post. Would love to hear your feedback! #MachineLearning #Python #Flask #DataScience #Projects #AI

🔗 GitHub Repository: [https://lnkd.in/gXa9zEBs] Strengthening Machine Learning concepts with Logistic Regression Covered practical implementation of: ✔ Binary Classification (Single & Multiple Inputs) ✔ Polynomial Logistic Regression ✔ Multiclass Classification (OVR & Multinomial) ✔ Decision Boundaries & Model Evaluation using Python and scikit-learn Understanding how logistic regression predicts probabilities and solves classification problems gives deeper insight into real-world ML applications. From theory to implementation, every project adds more clarity and confidence to the learning journey. #MachineLearning #LogisticRegression #Python #DataScience #ScikitLearn #GitHub

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics

🚀 Day 03 of My Machine Learning Journey: Understanding Array Shapes & Dimensions Today, I learned how NumPy arrays are structured using shapes and dimensions. I explored: ✅ What shape means in an array ✅ Difference between 1D, 2D, and 3D arrays ✅ How to check dimensions using `.shape` and `.ndim` Understanding data structure is key before moving into deeper Machine Learning concepts. 💡 #MachineLearning #NumPy #Python #LearningJourney #Day03

Today, I focused on working with NumPy arrays. Building a solid foundation for data manipulation and analysis. Here’s what I practiced: 🔹 Created a 1D array with values from 1 to 15 🔹 Built a 2D array (3×4) filled with ones 🔹 Generated a 3×3 identity matrix 🔹 Explored key array properties like shape, type, and dimensions 🔹 Converted a regular Python list into a NumPy array This session helped me better understand how data is structured and handled in numerical computing. Getting comfortable with arrays is definitely a crucial step toward more advanced data analysis and machine learning tasks. Looking forward to building on this momentum 💡 #AI #MachineLearning #Python #NumPy #DataAnalysis #M4ACE

🚀 Built my first RAG (Retrieval-Augmented Generation) Chatbot using Python! Instead of guessing, this chatbot reads, understands, and answers directly from custom data 📄➡️🤖 Powered by FAISS, HuggingFace embeddings, and Groq LLM, it delivers fast and context-aware responses. 💡 From static text → to intelligent conversations This is a small step into the world of AI-powered applications, but a big leap in how machines interact with knowledge. #AI #MachineLearning #LangChain #Python #RAG #GenAI #DataScience

Just a developer trying to make life a little more “hands-free.” ✋✨ I built a gesture-controlled mouse using Python and MediaPipe. It wasn’t easy—filtering out hand jitters and detecting a reliable “pinch” in real time took a lot of trial and error. The current setup: 🖱️ Pinch = Left Click 📜 Two-finger lift = Scroll 👍 Thumbs up = Volume Up Still experimenting and improving, but turning an idea into something that actually works feels great. You can also download the .exe file from the Releases section and try it directly. It’s simple, it’s experimental, and I’m learning something new with every line of code 📈 Try it here 👇 👉 https://lnkd.in/g9sZUCjM #CodingLife #SoftwareEngineering #AI #HandsOnLearning #PythonProject #Innovation #BuildInPublic