"Supervised Learning: A Core of Statistical Learning"

🍁 Experiment 7: Simple Linear Regression using Python 🤖 In this lab, I explored the fundamentals of Simple Linear Regression, one of the most widely used techniques in predictive modeling. 🔍 Key learning outcomes: • Understanding the relationship between independent and dependent variables • Implementing linear regression using scikit-learn • Evaluating model performance using metrics like MSE and R² This experiment enhanced my understanding of how regression helps in predicting continuous outcomes and serves as a foundation for advanced machine learning algorithms. 📁 Explore the repository here : https://lnkd.in/epWys7e7 #DataScience #MachineLearning #Python #ScikitLearn #Statistics #DataAnalysis #PredictiveModeling #LinearRegression #LearningJourney #JupyterNotebook Ashish Sawant

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

⚙️ 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

🍂 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

🧠 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

📊 SNAP is great, but what if you want to crunch data in Python or R? You can easily export SAR time series from SNAP and load them into pandas or xarray. Steps: 1. Preprocess in SNAP (calibration + terrain correction) 2. Use Export → CSV for selected ROIs 3. Load into Python (pandas.read_csv) 4. Run ML models, time series regression, or deep learning 💡 Blends GUI ease of SNAP with flexibility of Python. #SNAP #PythonGeospatial #SARTimeSeries #RemoteSensingAI #BigEOData #EarthObservation

Analysis of Variance (ANOVA): Analysis of Variance (ANOVA) is a statistical method used to compare the means of three or more groups to determine whether there are significant differences among them. It helps researchers understand if variations in data are due to actual differences between groups or just random chance. ANOVA is widely used in experiments, research studies, and data analysis to test hypotheses about group means. #statistics #datascience #datascience #ANOVA #analysisofvariance #dataanalytics #python #machinelearning #artificalintelligence

🚀 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

My new course on LinkedIn Learning - SQL Agents with Large Language Models, hit the 3000 learners milestone two weeks after the release, and it is now one of the popular courses on the platform! 🎉 Building a SQL AI agent is simpler than it sounds. If you’re curious about how SQL AI agents work and want to build one from scratch using Python, you can check it out here: https://lnkd.in/g4_W8na8 #AI #SQL #Python

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