Master data cleaning with Python—handle missing values, fix messy columns, and prep data for machine learning. Learn powerful Pandas tricks, automate your workflows, and clean text like a pro.
This course introduces fundamental concepts of data cleaning using Python, covering essential libraries, handling missing values, detecting and removing duplicates, dealing with outliers, and normalizing data for analysis.
Data Cleaning and Validation for Machine Learning with Python
4 lessons
Course 4
Automating Data Cleaning with Python
4 lessons
Course 5
Advanced Data Cleaning: Handling Text Data with Python
4 lessons
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This course explores advanced Pandas functionalities for transforming data, handling categorical and text data, processing date-time values, and performing feature engineering for better analysis.
This course ensures data integrity, feature selection, anomaly detection, and validation for ML models. The goal is to remove noisy, inconsistent, or biased data before training.
This course focuses on automating the data cleaning process using Python. It covers pipeline creation, automation with functions, handling large datasets efficiently, and logging and debugging.
This course extends data cleaning techniques to handle text-based data in tabular datasets. It covers cleaning and processing text columns, dealing with mixed data types, extracting meaningful features from text, and preparing text data for machine learning.
