Organize Data with Trees for Better Understanding

Day 6 of Solving ML Problems From Scratch: Adam Optimizer Today I worked on implementing the Adam Optimizer from scratch. What I like about Adam is that it combines the benefits of momentum and adaptive learning rates in a very practical way. Instead of taking the same type of step every time, it adjusts based on both past gradients and gradient magnitude, which makes optimization more stable and efficient. While solving this, I got a better understanding of: how momentum helps smooth the update direction how the velocity term adapts the step size why bias correction is important, especially in the early steps how Adam can converge faster than plain SGD in many cases Building these concepts from scratch is helping me understand what is really happening behind the libraries we use every day. It is one thing to call an optimizer in code, but it is very different to actually implement and reason through each update step yourself. Small daily practice like this is making machine learning feel much more intuitive. #MachineLearning #DeepLearning #ArtificialIntelligence #Python #DataScience

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

🚀 Built a Machine Learning model that predicts house prices. Most people stay stuck in tutorials. I decided to apply it. Used Linear Regression to train on real housing data, evaluated performance, and saved the model for reuse. 📊 Results: • R² Score: 0.58 • MSE: 0.56 Not perfect, but real learning happens here building, testing, improving. Pushed the complete project to GitHub 💻 #BuildInPublic #MachineLearning #AIJourney #Python #DataScience #Consistency #KeepLearning

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 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

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

‼️FREE SERIES ALERT Part 2: Framework to Implement Any ML Algorithm From Scratch (Python) | Full Beginner to Advanced AI This series is designed for beginners in AI/ML who want to move beyond "black-box" libraries and truly understand the software architecture expected in tech interviews. If you're preparing for ML roles and want to truly understand how algorithms work under the hood, this series is for you. https://lnkd.in/g6tQ9Y79

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

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

📊 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