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

Excited to share my latest project: LinearRegression-ML This is a beginner-friendly Machine Learning project focused on understanding and implementing Linear Regression from scratch. It includes practical notebooks like profit analysis and medical data predictions, along with clear explanations of loss and cost functions. ???What I learned =>Fundamentals of Linear Regression =>Cost & loss function implementation =>Real-world dataset analysis using Python #https://lnkd.in/guCQQdNe #MachineLearning #Python_Jupyter_Notebook #DataScience

🐍 Day 80 — Sampling and Population Day 80 of #python365ai 🧪 Population → entire dataset Sample → subset of data 📌 Why this matters: We usually analyse samples to infer properties of a population. 📘 Practice task: Take a small sample from a dataset and compute its mean. #python365ai #Sampling #Statistics #Python

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic

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

🚀 Excited to share my Machine Learning Project! 🏠 House Rent Prediction using Linear, Polynomial & Ridge Regression 🔹 Performed Exploratory Data Analysis (EDA) 🔹 Built and compared multiple regression models 🔹 Identified and fixed overfitting using Cross Validation 🔹 Improved model performance using Ridge Regression 📊 Key Insight: Even with high accuracy, cross-validation revealed overfitting — which I fixed using proper preprocessing. 🔗 Project Link: https://lnkd.in/ggMggCND #MachineLearning #Python #DataScience #StudentProject #CSE

Master Figures, Lines & Arrows in Matplotlib! The matplotlib module can plot geometric figures such as rectangles, circles, and triangles. These figures can then illustrate mathematical, technical, and physical relationships. This blog post demonstrates the creative options of matplotlib through three examples by illustrating the Pythagorean theorem: a gear representation, a pointer diagram, and a current-carrying conductor in a homogeneous magnetic field. #Python #DataViz #Matplotlib #CodeMagic #RheinwerkComputingBlog Dive in now and transform your graphs! https://hubs.la/Q04byPg90

📊 Understanding Joint Distributions in Probability Ever wondered how to model the relationship between two random variables? A joint distribution is the key! It describes the probability of two (or more) events happening simultaneously, giving us a complete picture of their interaction. In my latest Python experiment, I created a simple joint distribution table for two discrete variables, X and Y, representing the number of heads and tails in two coin flips. Here’s what I learned: Joint distribution tells us the probability of both X and Y taking specific values. Marginal distributions help us understand each variable independently. Conditional distributions show how one variable behaves given a specific value of the other. This concept is foundational in statistics, machine learning, and data science. It’s amazing how much insight we can gain from just a few lines of code! 🔗 Check out the code snippet in the comments if you’re curious to try it yourself. #Probability #Statistics #DataScience #Python #MachineLearning #Coding

Stop scrolling if you’ve ever wondered how people actually predict the future with data. I’ve been learning ARIMA forecasting recently, and I mapped out a simple roadmap that made everything click for me. It starts with getting comfortable in Python - Pandas for wrangling, Matplotlib for visualising. Then you move into the core ideas: stationarity, ACF, PACF, and how they shape the model. After that, it’s about building the ARIMA model, validating it properly, and using it to make real‑world predictions. What I enjoy most is how it turns raw, messy data into insights you can genuinely act on. Still learning, but enjoying the process 🚀 #DataScience #TimeSeries #ARIMA #Python #LearningJourney

Are your dataset variables secretly plotting behind your back? 👀 Before building any Machine Learning model, you need to know exactly how your features interact. Some are best friends, others are total strangers, and a few are just repeating the exact same story. How do you spot them instantly? Enter: The Correlation Matrix. 🔴🔵 It's not just a pretty heatmap—it's the ultimate lie detector for your data. Check out the post below to learn how to decode it in seconds! 👇 #DataScience #MachineLearning #DataAnalysis #Python #DataViz #Analytics #ScikitLearn #Coding #BigData #TechTips #ArtificialIntelligence #DataScientist #Statistics #EDA