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Feature Engineering for Machine Learning is a tutorial from Udemy site that introduces you to feature engineering in machine learning and teaches you how to convert variables into data and build better models. If you\’ve ever taken your first steps in data science and become familiar with prior models, you\’re likely to face more difficult challenges. At this point you may notice that your code looks cluttered and many values ​​are vague.

This course is a comprehensive course in feature engineering and variables for machine learning that teaches you many engineering techniques. In this course you will learn how to identify missing data, encoding definitive variables, converting numeric variables, deleting segments, managing time and date variables, working with different time zones, and managing composite variables and various application projects You solve it.

Courses taught in this course:

  • Learn different techniques to show missing data
  • Convert deterministic variables to numbers
  • Working with rare and unseen categories
  • Convert diagonal variables to Gaussian
  • Converting Numeric Variables to Discrete

Feature Engineering for Machine Learning course specifications:

  • English language
  • Duration: 9 hours and 47 minutes
  • Number of lessons: 123
  • Level of education: Intermediate
  • Instructor: Soledad Galli
  • File format: mp4

Course headings

123 lectures 09:47:53

9 lectures 20:03

Variable Types
6 lectures 15:43

Variable Characteristics
11 lectures 47:14

Missing Data Imputation
25 lectures 02:05:04

Multivariate Missing Data Imputation
1 lecture 00:03

Categorical Variable Encoding
21 lectures 02:04:38

Variable Transformation
4 lectures 23:05

14 lectures 01:10:17

Outlier Handling
7 lectures 33:06

Feature Scaling
14 lectures 52:22

Engineering mixed variables
2 lectures 09:23

Engineering datetime variables
3 lectures 17:32

Assembling a pipeline engineering feature
5 lectures 48:56

Final section | Next steps
1 lecture 00:21

Course prerequisites

  • A Python installation
  • Jupyter notebook installation
  • Python coding skills
  • Some experience with Numpy and Pandas
  • Familiarity with Machine Learning algorithms
  • Familiarity with Scikit-Learn


Engineering Feature for Machine Learning

Sample movie


Installation guide

View with your favorite Player after Extract.

English subtitle

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 361 MB

File password (s):


1.35 GB

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