Feature Engineering for Machine Learning is a training course from the Udemy site that introduces you to feature engineering in machine learning and teaches you how to convert variables to data and build better models. If you have taken your first steps in data science and are familiar with previous models, you are likely to face more difficult challenges over time. At this point, you may find that your code looks cluttered and many values are ambiguous.
This course is a comprehensive course in the field of feature and variable engineering for machine learning that teaches you many engineering techniques. In this course, you will learn how to identify lost data, encode definite variables, convert numeric variables, delete separate sections, manage time and date variables, work with different time zones, and manage composite variables, and various application projects. You solve.
Things to be taught in this course:
- Learn different techniques to show lost data
- Convert definite variables to numbers
- Work with rare and unseen categories
- Convert diagonal variables to Gaussian
- Convert numeric variables to separate
Features of Feature Engineering for Machine Learning course:
- English language
- Duration: 10 hours and 28 minutes
- Number of courses: 138
- Level of education: Intermediate
- Teacher: Soledad Galli
- File format: mp4
Feature Engineering for Machine Learning Course topics
Feature Engineering for Machine Learning 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
After Extract, watch with your favorite Player.
Version 2022/3 has increased by 15 lessons and duration of 41 minutes compared to 2019/3.