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Feature engineering in Python

feature engineering in Python. Join us and learn with our online course.

Language: English, Hindi

What you'll learn

  • Dealing with missing values

  • Dealing with skewness in data

  • Reading boxplots and handling outliers

  • Encoding categorical data

  • Data normalization

  • Splitting data into train, validation and test set

  • Working with imbalanced data

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₹ 12, 000

₹ 16,000  

Discount 25 % off

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This course includes

  • 1 Week

  • Classroom Training

  • Full lifetime access

  • Certificate of Completion

Course Content

Missing values

  • Dropping

  • Imputing with mean, median, mode

  • Exercises

Skewness

  • Concept

  • Log transforms

  • Square root transform

  • Box-cox transform

  • Exercises

Outliers

  • Reading boxplots

  • Detecting outliers

  • Removing outliers

  • Exercise

Encoding categorical data

  • Concept

  • Encoding with dummies

  • Label encoding

  • One-hot encoding

Data normalization

Binning data

Working with imbalanced data

  • Under sampling

  • Over sampling

  • SMOTE

Splitting dataset

Requirements

  • Interest to learn programming

  • Computer with internet access

  • A computer - Windows, Mac, and Linux are all supported. Setup and installation instructions are included for each platform

  • Basic English understanding

  • Basic Mathematical arithmetic

Description

Learn feature engineering the easy and fun way with our feature engineering course! You'll master the essentials of feature engineering and unlock the true power of machine learning.

 

This course will give you the tools to start converting unstructured data into features that can be used for machine learning. Discover how to perform feature engineering for your own machine learning models. Feature engineering is the process of translating raw data into features that are better understood by the machine learning algorithms, to ultimately get better performance.

 

In this course you will learn - Dealing with missing values, Dealing with skewness in data, Reading boxplots and handling outliers, Encoding categorical data, Data normalization, Splitting data into train, validation and test set, Working with imbalanced data and more!

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