Pandas vs Polars: Choosing the Right Data Tool

Day 2 of my ML journey 🚀 ✅ Watched Andrew Ng ML course ✅ Built Titanic Survival Prediction model ✅ Compared Logistic Regression (82%) vs Random Forest (84%) ✅ Submitted to Kaggle competition — scored 78.4% GitHub: https://lnkd.in/dFuZhjp7 #MachineLearning #Kaggle #Python #DataScience”

Recently started exploring Python in the AI ecosystem. One thing I really like about Python is how quickly you can move from idea to implementation. Example: A simple model predicting output from input data. from sklearn.linear_model import LinearRegression X = [[1], [2], [3]] y = [2, 4, 6] model = LinearRegression() model.fit(X, y) print(model.predict([[4]])) Just a small experiment, but it shows how machines can learn relationships from data. Excited to keep learning and building more with Python and AI. #Python #AI #MachineLearning #DeveloperLife

🚀 Day 43/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 7. Train Test Split 8. Correlation 9. Feature Selection Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience

Developed a simple Linear Regression model to predict real estate values based on year data. This model was built using Python and deployed via a Flask API, enabling predictions through API requests. Tools used: • Python • Scikit-learn • Flask API • NumPy • Postman This project explores the integration of machine learning models into APIs for real-world prediction systems. It has been a valuable learning experience while experimenting with @Uptor. #MachineLearning #Python #FlaskAPI #DataScience #AI #Learning

Ever wondered how to plan your week to study smarter, not harder? 🤔 I built my first AI Study Planner using Python! ✅ What it does: Takes your subjects, difficulty, importance, and available hours Calculates a priority score for each subject Suggests how much time to spend on each topic Visualizes priorities and hours with charts 🔹 Why it’s human-like: I added a bit of randomness in suggested hours to make it feel like a student experimenting with their study schedule. 💡 What I learned: Data preprocessing using pandas Normalizing values with scikit-learn Visualizations using matplotlib & seaborn Making a project interactive and human-friendly 💻 Try it yourself: Check the code & README: https://lnkd.in/guEREPrU Would love your thoughts or suggestions to make it even smarter! #Python #MachineLearning #AI #StudentProject #PortfolioProject #LearningByDoing #DataScience #StudyPlanner

🚀 Day 2 of my AI & Data Science Journey Today I learned some important basics of Python 🐍 • What are Data Types (int, float, string, boolean) • How to use Variables to store values • Different types of Operators • Type Casting (converting one data type into another) Slowly understanding how coding actually works 💻 Small steps, but moving forward every day 📈 #Day2 #Python #LearningJourney #DataScience #Beginner #Consistency #AI

🧠 A Simple but Real Machine Learning Workflow (From Data → Production) Many people think Machine Learning is just training a model in Python. But in real systems, ML is a pipeline, not a single step. Here’s a simplified workflow I often think about when building ML systems: This is where Machine Learning becomes a real product feature, not just an experiment. The real challenge in ML isn’t training models. It’s building a reliable pipeline that connects data, models, and applications together. #MachineLearning #DataEngineering #AppliedAI #Python #SQLServer #MLOps #SoftwareEngineering #AIWorkflow

✅ Numpy arrays.... Today in our Python class at FIT – Future Innovative Technology, we explored NumPy arrays and learned some really interesting concepts. We covered: • Arrays in NumPy • 2D Arrays • Array Dimensions • Array Shapes It was exciting to understand how NumPy helps in handling data efficiently and how multidimensional arrays work. Learning these concepts is making programming feel more practical and powerful, especially for data science and AI. Every day I’m discovering something new, and this journey of learning Python and AI is becoming more interesting and enjoyable. #Python #NumPy #AI #MachineLearning #LearningJourney #FutureInnovativeTechnology

Demo Video: YouTube Chatbot using RAG & LLMs Primary Post: https://lnkd.in/g4uFVXRc Code: https://lnkd.in/gehUmsXZ Nitish Singh CampusX #AI #GenerativeAI #LLM #RAG #Python #MachineLearning #CampusX

How Python still powers modern AI systems Despite rapid advances in AI frameworks and models, most of the work is still written in Python. From research labs to production systems, Python still holds value. With libraries like TensorFlow and PyTorch, and data tools like NumPy and Pandas, developers can build and deploy models efficiently. The Python ecosystem supports fast experimentation and scaling. Knowing Python means understanding the language behind data science and generative AI. It helps you move from using AI tools to building them. #python #datascience #ai #cheatsheet #ml #genai