Nikhil Gopi’s Post

📊 Why Do We Split Data in Machine Learning? One of the most important steps in building a reliable ML model is splitting the dataset correctly. Here’s the common approach: 🔵 Training Set (~70%) Used to train the model. The model learns patterns from this data. 🟡 Validation Set (~15%) Used to tune hyperparameters. Helps in improving performance and avoiding overfitting. 🟠 Test Set (~15%) Used only for final evaluation. It checks how well the model performs on unseen data. 💡 Why not train on 100% data? Because a model that performs well only on training data but fails on new data is not useful. Proper data splitting ensures: ✅ Better generalization ✅ Reduced overfitting ✅ Reliable performance evaluation Machine Learning isn’t just about building models — it’s about building models that work in the real world. Day ___ of my ML learning journey 🚀 #MachineLearning #DataScience #TrainTestSplit #MLJourney #Python

Machine learning from first principles becomes much clearer when you look at the basic training loop behind almost every model. Instead of thinking about complex libraries, imagine we have a simple regression model with parameters m and b. The goal is simply to adjust these parameters so the model’s predictions get closer to the real data. The learning process follows a simple cycle: #ForwardPass – the model takes the input data and produces a prediction. #ComputeLoss – we measure how far the prediction is from the true value. This error tells us how well or badly the model performed. #BackwardPass – using automatic differentiation (autodiff), the system computes gradients. These gradients tell us how each parameter contributed to the error. #ParameterUpdate – we slightly adjust the parameters using gradient descent so that the next prediction becomes a little better. #MachineLearning #Regression #Mathamatics #Python

Today I explored pair plots while working on a car price prediction dataset. Understanding relationships between features like mileage, condition, economy, and performance metrics makes a huge difference before building any machine learning model. What I learned: • Visualizing data helps reveal patterns and correlations early • Feature relationships guide better model decisions • Exploratory Data Analysis (EDA) is just as important as model training. #MachineLearning #DataScience #Python #Seaborn #EDA #LearningInPublic

Cross-validation is a critical technique for building reliable machine learning models. Relying on a single train-test split may lead to unstable or biased performance estimates. Cross-validation provides a more robust evaluation by testing the model on multiple data subsets. Key advantages of cross-validation include: More reliable performance measurement Reduced risk of overfitting Better model comparison Improved generalization assessment Common approaches include K-Fold Cross-Validation and Stratified K-Fold for imbalanced datasets. Applying structured validation techniques improves confidence in model performance before deployment. I am strengthening my validation practices to ensure that my models are both accurate and dependable. #DataScience #MachineLearning #CrossValidation #ModelEvaluation #Python #Analytics

# Day 2 of my Machine Learning learning-in-public journey # Topic: Train Data vs Test Data One of the most important concepts in Machine Learning is splitting data. Why don’t we train a model on all the data? Because a good model should not just memorize data — it should generalize to new, unseen data. 1) Training data • Used to teach the model • The model learns patterns from this data 2) Testing data • Used to evaluate the model • Helps us understand real-world performance # Key takeaway If a model performs very well on training data but poorly on test data, it usually means the model is overfitting. 🤔 Question for you What do you think will happen if we train a model using 100% of the data? 👇 I’ll share my answer in the comments. #MachineLearning #DataScience #LearningInPublic #MLBasics #Python

While studying Machine Learning this week I tried to summarize the overfitting problem in my notebook. The drawing represents three different situations when training a model: A model that is too simple It cannot capture the patterns in the data. This is known as high bias (underfitting). A balanced model It captures the underlying trend and generalizes well to new data. A model that is too complex It starts memorizing the training data instead of learning the real pattern. This is the classic overfitting problem (high variance). One analogy that helped me understand this concept during the course is the idea of a bowl of soup: • If the soup is too cold -> not good. • If it is too hot -> you cannot eat it. • The perfect soup is somewhere in the middle. Machine learning models face the same challenge: finding the right level of complexity to generalize well. Also, studying this reminded me that sometimes learning Machine Learning means reopening algebra books from almost 15 years ago. Back to the fundamentals. #MachineLearning #ArtificialIntelligence #Python

🚀 Day 49/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Regression Algorithm 1: Linear Regression Today, I explored Linear Regression, one of the most fundamental algorithms used in machine learning for regression problems. It helps model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. Linear Regression is widely used for predictive analysis, such as forecasting sales, predicting house prices, estimating demand, and analyzing trends in data. One of the key advantages of Linear Regression is its simplicity and interpretability, making it a great starting point for understanding regression techniques in machine learning. Through this learning, I also practiced model training, prediction, and performance evaluation using metrics like Mean Squared Error (MSE) and R² Score. The journey continues as I explore more regression algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

 Thrilled to share my latest project: Gradient Descent in Machine Learning In this project, I implemented linear regression using gradient descent from scratch and compared it with scikit‑learn’s LinearRegression. 💡 Why it matters: Gradient descent is the foundation of many machine learning algorithms. By building it step by step, I gained a deeper understanding of how models learn, how learning rate affects convergence, and how optimization drives accurate predictions. ## Skills demonstrated: Python programming Data handling with Pandas & NumPy Visualization with Matplotlib Model comparison using Scikit‑Learn ## Key outcomes: My custom gradient descent achieved coefficients close to scikit‑learn’s model Visualized cost function convergence over iterations Strengthened my ability to debug, optimize, and explain ML workflows clearly ## Impact: This project sharpened my ability to translate mathematical concepts into working code — a skill that’s critical for building scalable, real‑world machine learning solutions. ## Explore the full project here: https://lnkd.in/g8r3QCf9 #MachineLearning #Python #DataScience #GradientDescent #GitHubProjects

Understanding ColumnTransformer in Machine Learning When working with real-world datasets, we often have numerical + categorical features together. Applying the same preprocessing to all columns is not correct. That’s where ColumnTransformer from scikit-learn comes in! 🔹 It allows you to apply different transformations to different columns in a single pipeline. 🔹 It keeps preprocessing clean, organized, and production-ready. 🔹 It avoids data leakage when used with Pipeline. Example: Apply Standardization to numerical features Apply OneHotEncoding to categorical features Combine everything into one transformed dataset This makes your ML workflow: ✔️ Cleaner ✔️ More efficient ✔️ Scalable 💬 Question: Have you used ColumnTransformer in your ML projects? What challenges did you face? Github : https://lnkd.in/dee_ZATE #MachineLearning #DataScience #Python #ScikitLearn #FeatureEngineering

🚀 Day 48/100 – Python, Data Analytics & Machine Learning Journey 🤖 Module 3: Machine Learning 📚 Today’s Learning: Supervised Learning – Classification Algorithm 5: Random Forest Today I explored Random Forest, a powerful ensemble learning algorithm used for classification and regression tasks. Random Forest works by building multiple decision trees during training and combining their predictions to produce a more accurate and stable result. One of the key advantages of Random Forest is its ability to reduce overfitting and handle large datasets with higher accuracy. It also works well with both numerical and categorical data. Random Forest is widely used in real-world applications such as fraud detection, recommendation systems, medical diagnosis, and customer behavior analysis. The journey continues as I explore more algorithms and their real-world applications. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience