This course focuses on teaching learners how to conduct exploratory data analysis efficiently. By the end of the course, students will be able to perform advanced house price prediction, feature engineering, and selection. They will also learn to use various automated EDA libraries like Autoviz, Pandas Visual Analysis, D-Tale, DataPrep, and Lux. The course covers different types of feature transformations in machine learning and guides students in building automated EDA projects. The intended audience for this course includes data scientists, machine learning enthusiasts, and anyone interested in mastering EDA techniques using Python libraries.
Overview
Syllabus
Advance House Price Prediction- Exploratory Data Analysis- Part 1.
Advance House Price Prediction- Exploratory Data Analysis- Part 2.
Advance House Price Prediction-Feature Engineering Part 1.
Advance House Price Prediction-Feature Engineering Part 2.
Advance House Price Prediction-Feature Selection.
Autoviz-Automatically Visualize Any Dataset With Single Line Of Code.
All Automated EDA Libraries All At One Place.
Pandas Visual Analysis- Perform Exploratory Data Analysis In A Single Line Of Code.
D-Tale The Best Library To Perform Exploratory Data Analysis Using Single Line Of Code.
Discussing All The Types Of Feature Transformation In Machine Learning.
Perform Exploratory Data Analysis In Minutes- Data Science| Machine Learning.
Building Automated Exploratory Data Analysis Project- Project From Subscriber.
DataPrep Library- Perform Faster EDA Within No Time.
Lux - Python Library for Intelligent Visual Discovery.
Clean, Analyze, Visualize And Perform EDA With Klib Library In No Time ️️️️.
Taught by
Krish Naik
