Zhanar Orynbassar’s Post

🌸 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

🚀 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

Master NumPy: The Backbone of Data Science Whether you are cleaning data, building neural networks, or performing complex simulations, NumPy is the foundation every Data Scientist needs to master. We know how overwhelming documentation can be. That’s why Antara and me at NeuroxSentinel designed this comprehensive NumPy Cheat Sheet to streamline your workflow. What’s inside? ✅ Array Creation & Manipulation ✅ Linear Algebra & Statistical Functions ✅ Trigonometric & Exponential Operations ✅ Bitwise, Random, & Fourier Transforms ✅ Set Operations and Miscellaneous Utilities Save this post for your next project or share it with a peer who’s diving into Python! #DataScience #Python #NumPy #MachineLearning #NeuroxSentinel #TechEducation #DataAnalytics

🚀 Day 25/100 – #100DaysOfML Today I explored Support Vector Machine (SVM) in Machine Learning. SVM is a powerful supervised learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that separates data into different classes. 🔹 What I learned today: • How SVM works • What support vectors are • The concept of margin and hyperplanes • Implementing SVM using Python and Scikit-learn SVM is especially useful when working with high-dimensional datasets and complex classification problems. Continuing my journey of learning and sharing through the 100 Days of Machine Learning challenge. #MachineLearning #DataScience #AI #Python #SVM #LearningInPublic

🚀 Day 5 of My Machine Learning Journey Today I learned the fundamentals of Probability, which plays a key role in Machine Learning. 📚 What I learned: • Basics of probability and how to measure likelihood • Conditional probability and its applications • How ML models use probability to make predictions 💻 Practical: Simulated probability scenarios using Python, including dice roll experiments and calculating event probabilities. Understanding probability is helping me see how models handle uncertainty and make intelligent predictions. #MachineLearning #Probability #AI #TensorFlow #DataScience #LearningJourney

🚀 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

The Foundation You Can't Ignore in Machine Learning 🧠 ✅ From Zero to Hero: It starts with the absolute basics (what is a scalar?) and gently guides you to advanced topics like Eigenvalues and Singular Value Decomposition (SVD). ✅ Code-First Approach: Every concept, from the dot product to matrix inversion, is accompanied by a clear NumPy example. You can literally code along as you read. ✅ ML Relevance: It doesn't leave you thinking, "Okay, but why does this matter?" It explicitly ties operations to algorithms, like using matrix inversion for Linear Regression or eigenvectors for PCA. If you've ever felt that "math for ML" is too abstract or intimidating, this tutorial is for you. It demystifies the numbers and shows you the beautiful logic that powers our favorite algorithms. 👉 Read the full guide here:  https://lnkd.in/eXifwmQx #MachineLearning #DataScience #ArtificialIntelligence #LinearAlgebra #Python #Mathematics #Programming #NumPy #DataEngineering #TechEducation #DevGenius #TowardsDataScience #MLBasics #Coding #AI

 Entering the World of Numerical Python: Day 46/100 📊🚀 To master AI, you must first master the Matrix. 🏗️ For Day 46, I’ve officially started my journey with NumPy—the backbone of Data Science and Machine Learning. Today, I moved beyond standard Python lists to explore N-Dimensional Arrays (ndarrays). Technical Highlights: 🏗️ Vectorized Operations: Learning how NumPy performs calculations across entire datasets without slow 'for' loops (Broadcasting). 🖼️ Image Logic: Visualizing how digital images are represented as matrices of pixel values. 📈 Statistical Analysis: Utilizing NumPy’s built-in functions to instantly calculate Mean, Max, and Sum of complex arrays. The Shift: Standard Python lists are for general tasks, but NumPy is for Performance. In the AI/ML world, speed is everything. By learning how to manipulate data at the hardware level with NumPy, I'm building the skills needed to handle massive datasets and complex neural networks. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #NumPy #DataScience #100DaysOfCode #BTech #AIML #Python #SoftwareEngineering #Mathematics #LearningInPublic #WomenInTech

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