Continuing my journey through my 𝐌𝐏𝐡𝐢𝐥 𝐀𝐈 Machine Learning course with a hands-on implementation of 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧.⁣⁣ ⁣⁣ The goal was to build a binary classifier from scratch. I implemented the core components in pure Python, focusing on:⁣⁣ ① 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬: Using the Sigmoid function to transform a linear output into a probability (a value between 0 and 1).⁣⁣ ② 𝐂𝐨𝐬𝐭 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: Implementing Log Loss (Binary Cross-Entropy) to measure the performance of the probability predictions.⁣⁣ ③ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫: Applying Gradient Descent to find the optimal parameters by minimizing this new cost function.⁣⁣ ⁣⁣ It’s a great lesson in how a few core mathematical concepts can be combined to build a powerful classification model from the ground up. On to the next topic! 🚀⁣⁣ ⁣⁣ #ArtificialIntelligence #MachineLearning #LogisticRegression #Python #DataScience #FromScratch #MPhil #Classification #SigmoidFunction

A mini project about Supervised Learning, applied it by predicting house prices using the California Housing Dataset from Kaggle. Tools: Python, Pandas, Scikit-learn, Matplotlib Steps: Cleaned and visualized the dataset Trained a Linear Regression model Evaluated using mean squared error and r2 score Achieved an RMSE of 69,297.72 and visualized predictions vs actual prices. GitHub: https://lnkd.in/d8CkpV_b #MachineLearning #DataScience #Python #LearningJourney #AI

⚙️ Experiment 10: Support Vector Machine (SVM) using Python 🤖 In this lab, I explored the Support Vector Machine (SVM) algorithm — one of the most robust and widely used supervised learning models for classification and regression tasks. 🔍 Key learning outcomes: • Understanding the concept of hyperplanes and margins in classification • Implementing SVM using scikit-learn • Exploring linear and non-linear (kernel-based) decision boundaries • Performing hyperparameter tuning for improved accuracy • Visualizing classification boundaries and model performance This experiment helped me understand how SVM achieves high accuracy and generalization by optimizing the decision boundary, making it ideal for complex real-world datasets. 📁 Explore the repository here : 👉 https://lnkd.in/epWys7e7 #DataScience #MachineLearning #Python #SVM #Classification #KernelMethods #PredictiveModeling #DataAnalysis #LearningJourney #JupyterNotebook Ashish Sawant sir

👉 🚀 Hands-on Machine Learning Project: Linear Regression 🧠 Excited to share my latest project — Linear Regression Model built in Python (Jupyter Notebook)! 🎯 In this project, I explored how to predict house prices based on house size using one of the most fundamental algorithms in Machine Learning — Linear Regression. This project helped me understand: ✅ How the model finds the best-fit line ✅ The relationship between features and target variables ✅ How to visualize and interpret predictions 🔗 Check out my full project on GitHub: 👉 https://lnkd.in/dM6f7ik8 #MachineLearning #DataScience #Python #LinearRegression #GitHub #DataAnalytics #AI #LearningByDoing #WomenInTech #CareerGrowth

🧠 Day 78 — Scikit-learn Base Jupyter Notebook Today I learned how to build a simple Machine Learning model using Scikit-learn in Jupyter Notebook. From loading data to saving the trained model — this covered the full ML workflow. I used the “tips” dataset, prepared the data, trained a Linear Regression model, made predictions, and evaluated it using MAE and R² Score. Finally, I saved the model using pickle for future use. This practice helped me understand the complete process of creating, testing, and saving an ML model in Python. ✨ #Day78 #MachineLearning #ScikitLearn #Python #DataScienceJourney #LearningEveryday

📚 Supervised learning is at the core of statistical learning, where the goal is to train a model using labeled data. The model learns from these labeled data to predict the output for new, unseen data. 📌DOWNLOAD FREE PDF HERE 🔜 https://lnkd.in/d5EmK2C6 The Elements of Statistical Learning offers a comprehensive framework for analyzing and interpreting data using statistical and machine learning techniques. #python #pythonprogramming #pythondevelopment #growthifyme

🚀 Today, I explored some more about NumPy! NumPy is the backbone of numerical computing in Python, and it’s incredible how much we can achieve with just a few lines of code. 💻✨ Efficient array and matrix manipulations Powerful mathematical and statistical functions Essential for data science, ML, and AI projects Some more about what I tried: Calculated matrix determinants and inverses Practiced matrix multiplication and element-wise operations Explored reshaping and stacking arrays for better data handling Excited to keep building my Python and data skills with practical hands-on examples! #Python #NumPy #DataScience #MachineLearning #LearningJourney

🍂 Experiment 8: Logistic Regression using Python ⚙️ In this lab, I explored Logistic Regression, a fundamental algorithm for binary classification problems in machine learning. 🔍 Key learning outcomes: • Understanding the concept of logistic (sigmoid) function and decision boundaries • Implementing Logistic Regression using scikit-learn • Visualizing classification results and interpreting probabilities This experiment strengthened my grasp of classification techniques and how Logistic Regression forms the foundation for many real-world applications like spam detection, disease prediction, and customer segmentation. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience  #MachineLearning  #Python  #LogisticRegression  #ScikitLearn  #Classification #PredictiveAnalytics  #LearningJourney  #JupyterNotebook Ashish Sawant Sir

📶 Experiment 9: K-Nearest Neighbors (KNN) Algorithm using Python 📊 In this lab, I explored the K-Nearest Neighbors (KNN) algorithm — a simple yet powerful instance-based learning technique used for both classification and regression tasks. 🔍 Key learning outcomes: • Understanding the concept of distance-based classification • Implementing KNN using scikit-learn • Choosing the optimal value of K for better accuracy • Evaluating model performance using various metrics • Visualizing decision boundaries and classification outcomes This experiment deepened my understanding of how KNN leverages similarity between data points to make accurate predictions, emphasizing the importance of feature scaling and data normalization. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience #MachineLearning  #Python  #KNN  #ScikitLearn  #Classification  #DataAnalysis  #PredictiveModeling  #Statistics  #LearningJourney  #JupyterNotebook Ashish Sawant Sir

Unlocking the power of data with NumPy! From mean and median to standard deviation and correlation — understanding basic statistics in NumPy is the first step to mastering data analysis in Python. Using functions like np.mean(), np.median(), np.std(), and np.corrcoef() makes statistical computation fast and reliable. Data-driven decisions start with understanding the basics. #NumPy #Python #MachineLearning #Statistics #DataScience #CodingJourney #CodeNewbie #LearningJourney #DataScienceJourney #AI #DataAnalytics #ArrayinNumpy #ArrayManipulation