Machine Learning Pipeline in Python : From Raw Data to Deployed Model
Build a complete machine learning pipeline in Python from data cleaning and feature engineering to model training, evaluation, and deployment.
Build a complete machine learning pipeline in Python from data cleaning and feature engineering to model training, evaluation, and deployment.
Learn what NLP (Natural Language Processing) is and how it helps machines understand human language. Includes real-world examples and clear beginner explanations.
Learn how to visualize data like a pro using Python’s Matplotlib, Seaborn, and Plotly. Discover when to use each, how to plot common charts, and best practices for clear, effective data storytelling.
Understand inheritance and polymorphism in Python with detailed examples. Learn how to reuse code, override methods, and write flexible, object-oriented programs.
Feature engineering is the secret weapon of great models. Learn how to transform raw data into high-impact variables that help your models perform better.
Learn how to use super() in Python to reuse and extend parent class methods. A beginner-friendly guide with real examples and best practices.
Explore this massive list of free datasets for data science, machine learning, NLP, and computer vision plus project ideas, downloads, and tools.
Learn pandas from scratch. Clear guide covering DataFrames, Series, indexing, filtering, and basic operations with practical examples.
Learn step-by-step how to clean messy, missing, and incorrect data using pandas. Reliable insights start with clean data.
Learn what __init__ in Python really does, how it works, and why it’s important. This short guide will help you master object initialization with clear examples.