Sign in Subscribe
7 min read python

Interactive Python Series - Lists

Lists are the workhorses of Python data storage, allowing you to keep ordered collections of items. We’ll dive into indexing, slicing, and essential methods like .append() and .pop() to manage your data.
Interactive Python Series - Lists
Interactive Python Tutorial

Tutorial Loading...

Loading Kernel...
-- / --

Introduction

main.py
Terminal Output
Initializing System...

Published by:

WassupAI

You might also like...

07
Dec
Interactive SkLearn Series - Evaluation Metrics (Classification)

Interactive SkLearn Series - Evaluation Metrics (Classification)

Accuracy isn't everything. We’ll dive into Precision, Recall, and F1-Score for imbalanced datasets, and use ROC-AUC and Confusion Matrices to fully diagnose classifier performance.
10 min read
07
Dec
Interactive SkLearn Series - Nearest Neighbors

Interactive SkLearn Series - Nearest Neighbors

Predict based on proximity. We’ll use K-Nearest Neighbors (KNN) for classification, understanding how distance metrics work and why the "curse of dimensionality" affects performance.
7 min read
07
Dec
Interactive SkLearn Series - Tree-Based Models

Interactive SkLearn Series - Tree-Based Models

Mimic human decision-making. We’ll visualize tree structures, understand splitting criteria like Gini impurity, and learn how to prune trees to prevent overfitting.
9 min read
07
Dec
Interactive SkLearn Series - Support Vector Machines

Interactive SkLearn Series - Support Vector Machines

Master the hyperplane. We’ll explore how SVMs use kernels (Linear, RBF, Poly) to project data into higher dimensions, allowing for precise separation of complex, non-linear classes.
10 min read
07
Dec
Interactive SkLearn Series - Linear Classifiers

Interactive SkLearn Series - Linear Classifiers

Classify data with speed. We’ll compare Logistic Regression for probability estimation against the SGDClassifier, which efficiently handles massive datasets using gradient descent.
9 min read

Member discussion