🚀 Machine Learning Learning Journey Today I worked on a hands-on project implementing Logistic Regression for a binary classification problem. In this exercise, I practiced important machine learning concepts including: 🔹 Train-Test Split 🔹 Logistic Regression Model Training 🔹 Model Prediction 🔹 Model Evaluation Using Python, Pandas, and Scikit-learn, I trained a logistic regression model to classify data and evaluate its performance on unseen data. This project helped me better understand how machine learning models are trained and tested using real datasets. 📂 GitHub Repository: https://lnkd.in/g_ns8aEN Currently continuing my learning journey in Machine Learning and building projects to strengthen my data science skills. #MachineLearning #Python #DataScience #AI #LearningJourney #ScikitLearn

We all use optimisers in Machine Learning — but how often do we actually see them working? I built Gradient Descent from scratch in Python, implementing: • Vanilla Gradient Descent • Momentum • Learning Rate Decay • RMSprop No ML libraries. Just NumPy, math, and curiosity. I visualised the entire training process — loss curves, weight & bias updates, parameter movement, and even full training animations. Watching the line slowly move toward the true parameters makes the theory feel real. Big takeaway? Optimisers aren’t magic. They’re disciplined update rules applied repeatedly. I did take GPT’s help in structuring parts of the code — AI speeds things up, but real understanding comes from building and experimenting yourself. Code here: https://lnkd.in/d4maNaR4 #MachineLearning #GradientDescent #Python #AI #LearningInPublic #DeepLearning #NeuralNetworks #ArtificialIntelligence #MLResearch #LearningDynamics #Optimization #GradientDescent

🚀 Starting my journey in AI & Machine Learning Currently learning the fundamentals and practicing basic data operations using Pandas in Python. In this video, I worked with a sample hotel dataset to explore: • Reading CSV files • Understanding datasets using head(), tail(), info(), and describe() • Filtering data • Dropping rows and columns • Using loc[] and iloc[] for indexing Building strong foundations before moving into Machine Learning projects. #Python #Pandas #MachineLearning #ArtificialIntelligence #PythonDeveloper #FullStackDeveloper

🌸 What better way to start learning Machine Learning than with the classic Iris dataset? For my first ML project, I built an Iris Flower Classifier using Support Vector Machine (SVM) in Python. Here’s what I worked on: 🔹 Loaded and explored the Iris dataset (150 samples, 4 features) 🔹 Performed statistical analysis using df.describe() 🔹 Visualized feature relationships using Seaborn pairplots 🔹 Split the dataset into features (X) and labels (y) 🔹 Trained a classification model using Scikit-learn’s SVC The model learns to classify three species Setosa, Versicolor, and Virginica using just four measurements. 📊 Result: The model achieved 96% accuracy on the test dataset. 🎥 Here’s a short video showing the project and how it works. Excited to continue learning and building more ML projects. 🚀 #MachineLearning #Python #DataScience #SVM #AI #LearningJourney #100DaysOfCode

What if you could estimate your CGPA before results? 👀 I built a Machine Learning model to simulate and predict CGPA using a synthetic dataset (500+ records). 📈 R² Score: 0.904 📊 Mean Absolute Error (MAE): 0.104 🧠 Linear Regression based approach This project helped me understand data preprocessing, model training, and evaluation metrics in a real ML workflow. Sharing a quick demo below — feedback welcome! 🚀 #MachineLearning #Python #DataScience #AI #StudentProject

Forecasting is a fundamental data science task because time series datasets are prevalent in science and business. The field has evolved in past years, by integrating machine learning models to the established toolkit of statistical approaches. Forecasting: Principles and Practice is a popular book about time series analysis and forecasting. Recently, a new version based on Python has also been released, now including a chapter about foundation models! You can visit the link below for more information, and make sure to follow us for regular data science content. 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: https://otexts.com/fpppy/ 𝗔𝗜 𝗡𝗲𝘄𝘀 & 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀: https://lnkd.in/dvcgY5Ws #AI #deeplearning #forecasting #python

🚀 Day 24/100 – #100DaysOfML Today I explored the K-Nearest Neighbors (KNN) algorithm in Machine Learning. KNN is one of the simplest supervised learning algorithms and works by classifying data points based on the closest neighbors in the dataset. 🔹 What I learned today: • How the KNN algorithm works • The importance of choosing the right K value • How distance metrics influence predictions • Implementing KNN using Python and Scikit-learn KNN is a great algorithm for beginners because it clearly shows how similar data points influence predictions. Continuing my journey of learning and sharing through the 100 Days of Machine Learning challenge. #MachineLearning #DataScience #AI #Python #KNN #LearningInPublic

🚀 Day-63 of #100DaysOfCode 📊 NumPy Practice – Eigenvalues & Eigenvectors Today I explored an important Linear Algebra concept using NumPy. 🔹 Concepts Practiced ✔ Matrix operations ✔ np.linalg.eig() ✔ Eigenvalues & eigenvectors ✔ Mathematical foundations of Machine Learning 🔹 Key Learning Eigenvalues and eigenvectors play a crucial role in Dimensionality Reduction techniques like PCA and many machine learning algorithms. Learning how mathematics connects with real-world data science problems 📊✨ #Python #NumPy #LinearAlgebra #MachineLearning #DataScience #100DaysOfCode

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

Ever wonder how to truly trust your Machine Learning model's performance? 🤔 Relying on a single train-test split can sometimes be a bit misleading. I’ve been diving deeper into model evaluation techniques using Python and Scikit-Learn. In this quick snippet, I’m exploring K-Fold Cross-Validation to rigorously compare a few classic algorithms (Logistic Regression, SVM, and Random Forest) on the digits dataset. I started by building the cross-validation loop manually to really understand the mechanics under the hood, and then transitioned to using cross_val_score for a much cleaner, more efficient approach. It's fascinating to see the variance in accuracy across different folds! 📊 Building a strong foundation in these core data science concepts is so crucial. What’s your go-to method for evaluating model performance? Let me know below! 👇 #MachineLearning #DataScience #Python #ScikitLearn #CrossValidation #ArtificialIntelligence