Feature Engineering
Dive into a comprehensive 13-hour course on Feature Engineering, covering essential techniques for data preprocessing in machine learning. Learn how to perform One Hot Encoding for multi-categorical variables, explore different encoding methods, and understand the importance of feature scaling. Master handling missing values in categorical features, dealing with high-cardinality categories using Count/Frequency Encoding, and implementing Ordinal Encoding for ordinal categories. Participate in live sessions covering various techniques for missing value imputation, categorical feature handling, and Probability Ratio Encoding. Explore standardization and transformation techniques, discuss strategies for handling imbalanced datasets, and examine the impact of outliers on machine learning use cases. Gain insights into various feature transformation types and learn a step-by-step process for Exploratory Data Analysis (EDA) and Feature Engineering in data science projects.