Jitendra Kumar’s Post

🐍 Day 89 — Features and Labels Day 89 of #python365ai 📌 Features (X) → input variables Labels (y) → output Example: X = [size, rooms] y = price 📌 Why this matters: Clear distinction is essential for building ML models. 📘 Practice task: Identify features and labels in a dataset. #python365ai #Features #MachineLearning #Python

You can fit the most common Bayesian regression models in Python using a consistent syntax (similar to brms in R) using the bambi package. It utilizes PyMC to do the simulations. It's remarkably easy and straightforward to use - you just adjust the family name to the right model type. Here are a few examples. More instructions are available here: https://lnkd.in/eGSG3-Bk #statistics #datascience #analytics #rstats #python #peopleanalytics #technology #ai

#MachineLearning #Python #AI #DataScience #Pickle After building your AI model, the training phase can take a long time, and you may close VSCode. It is not logical to train the model again every time you run your code. This is where Python’s pickle module becomes invaluable. It allows us to serialize (save) and deserialize (load) Python objects, including our AI model. With model.pickle, we don’t need to train the model again next time — we just load it and use it directly.

sqrt() calculates the square root of a number. After calculating the root, the result participates in the rest of the arithmetic expression. 📌 Answer: B) 6 #Python #MathFunctions #PythonChallenge #LearningInPublic #CodingPractice

Hot take: the overhead to learn more abstractions is only useful when the value is 10x more than the investment of learning time. That's why simple abstractions that get out of the way are better. Senior engineers figured this out already and that's why they keep abandoning agent frameworks for while loops in plain Python. They don't want a framework. They want primitives. #AgentFrameworks #LLM #Python

🎬 Built a Movie Recommendation System using Python & ML Using content-based filtering to recommend movies based on similarity — here's a quick breakdown how it works: 1. TF-IDF Vectorizer converts movie descriptions into vectors. 2. Cosine Similarity measures how similar two movies are. 3. Random Forest classifier validates the results. Results: ◈ 16 movies ◈ 4 genres ◈ 97% model accuracy ◈ Toy Story & Finding Nemo topped similarity at 0.61 ◈ The Godfather & Goodfellas closely matched at 0.58 A great way to understand how Netflix-style recommendations work under the hood. Open to feedback and questions! 👇 #MachineLearning #Python #DataScience #RecommendationSystem #BuildInPublic #dataanalytics #datascience #mlproject #datanalyst #datascientist #scikitlearn

Built Logistic Regression from Scratch using NumPy! Implemented the sigmoid function, trained the model using gradient descent, and visualized the logistic curve for binary classification. This project helped me understand how logistic regression actually works under the hood without using ML libraries. 🔗 GitHub: https://lnkd.in/gnZ-g4aQ #MachineLearning #Python #NumPy #LogisticRegression #DataScience #LearningInPublic

What’s the biggest dataset you’ve ever scraped? Curious about this. A lot of scraping discussions focus on techniques, but not much on scale. What’s the largest dataset you’ve personally collected from scraping? Thousands of pages? Millions? Also curious what problems started appearing once things got bigger. #WebScraping #BigData #DataEngineering #Python #DataScience #WebData

🐍 Day 93 — Linear Regression (Implementation) Day 93 of #python365ai 🧑💻 Example: from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) 📌 Why this matters: This is your first real ML model. 📘 Practice task: Fit a simple regression model. #python365ai #MLModel #Python #DataScience

Hi, friends! I just built my first AI-powered stock prediction app from scratch — as a complete Python beginner. It uses Machine Learning to predict stock price direction for any ticker in the world. Built with Python, scikit-learn, yfinance and Streamlit. Check it out https://lnkd.in/eXAZW4U8 #Python #MachineLearning #DataScience #100DaysOfCode #PythonProgramming