Creating example datasets has never been this easy. With the drawdata library in Python, you can sketch your data and turn it into a dataset in seconds. You can create clusters, trends, and outliers exactly the way you need. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #datascience #python #machinelearning #statistics #dataanalysis #datavisualization #programming #ai #statisticsglobehub

Creating example datasets has never been this easy. With the drawdata library in Python, you can sketch your data and turn it into a dataset in seconds. You can create clusters, trends, and outliers exactly the way you need. I just released a new module on this in the Statistics Globe Hub: https://lnkd.in/exBRgHh2 #datascience #python #machinelearning #statistics #dataanalysis #datavisualization #programming #ai #statisticsglobehub

Today, I learned how to take user input in Python using the input() function. This allows programs to interact with users and collect data such as name, age, and city. I also learned how to convert input into numbers using int() and float(), which is very important for calculations and data processing. #Day2 #Python #LearningJourney #DataScience #MachineLearning #Consistency

Built Linear Regression from scratch using Python (no libraries) Wanted to understand what’s happening under the hood before moving to sklearn. So I implemented a simple model to predict marks based on hours studied using Gradient Descent. 🔹 What I did: Implemented the prediction function (y = wx + b) Calculated Mean Squared Error (MSE) manually Computed gradients and updated parameters over 1000 epochs 🔹 What I learned: How gradient descent updates weights step by step Why learning rate plays a critical role How loss decreases as the model learns 🔹 Result: The model successfully learned the relationship. Example: If a student studies 9 hours → predicted marks ≈ 89.3 🔗 Code: https://lnkd.in/gPHCenhB Next step: implementing this using NumPy and then sklearn. #MachineLearning #Python #LearningInPublic

Learn machine learning with Python and discover how to apply it to real-world problems with this comprehensive guide #MachineLearningWithPython Read the full article

Day 9 of #111DaysOfLearningForChange – Code for Change Today I learned about List comprehesion. List comprehension is a concise and elegant way to create lists in Python. It allows you to transform or filter data from an existing iterable (like a list, tuple, or range) and pack it into a new list, usually in a single line of code. #111DaysOfLearningForChange #CodeForChange #Day9LearningForChange #Python #DataScience #AI/ML

🐍 Day 117 — Hyperparameter Tuning Day 117 of #python365ai ⚙️ Tune model settings to improve performance. Example: from sklearn.model_selection import GridSearchCV 📌 Why this matters: Small changes can significantly improve results. 📘 Practice task: Tune one parameter in a model. #python365ai #HyperparameterTuning #ML #Python

Back to consistency 💻🚀 Recently, I worked on implementing Pascal’s Triangle in Python — and it turned out to be a great exercise in logic building. While solving this, I learned: 🔹 How each row depends on the previous one 🔹 Better understanding of nested loops 🔹 Using mathematical logic instead of brute force It’s interesting how such a simple-looking pattern involves deeper thinking behind the scenes. Here’s my implementation 👇 Small steps like these are helping me build a strong foundation in Data Structures & Algorithms. #Python #DSA #CodingJourney #LearningInPublic #100DaysOfCode

Theory is great, but execution matters. Check out this 60-second visual breakdown on how to actually code outlier detection in Python. We're using pandas and scipy to implement both IQR Capping and Z-Score Dropping so your models stop skewing. #Part2 #DataScience #MachineLearning #Python #Pandas #DataEngineering #DataCleaning #TechEducation

🐍 Day 116 — Cross Validation Day 116 of #python365ai 🔁 Cross-validation splits data multiple times. Example: from sklearn.model_selection import cross_val_score 📌 Why this matters: Provides more reliable performance estimates. 📘 Practice task: Run cross-validation on a model. #python365ai #CrossValidation #MachineLearning #Python