What you’ll learn
Learn multiple techniques for missing data imputation
Transform categorical variables into numbers while capturing meaningful information
Learn how to deal with infrequent, rare and unseen categories
Transform skewed variables into Gaussian
Convert numerical variables into discrete
Remove outliers from your variables
Extract meaningful features from dates and time variables
Learn techniques used in organisations worldwide and in data competitions
Increase your repertoire of techniques to preprocess data and build more powerful machine learning models
Requirements
A Python installation
Jupyter notebook installation
Python coding skills
Some experience with Numpy and Pandas
Familiarity with Machine Learning algorithms
Familiarity with Scikit-Learn
Description
NEW! Updated in November 2020 for the latest software versions, including use of new tools and open-source packages, and additional feature engineering techniques.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online . In this course, you will learn how to engineer features and build more powerful machine learning models.
Who is this course for?
So, you’ve made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you’re probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can’t find many consolidated resources about feature engineering. Maybe even just blogs? So you may start to wonder: how are things really done in tech companies?
This course will help you! This is the most comprehensive online course in variable engineering . You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.
mega.nz
Key : XOQA-9hzFSrvRv1_w7yYHw
Learn multiple techniques for missing data imputation
Transform categorical variables into numbers while capturing meaningful information
Learn how to deal with infrequent, rare and unseen categories
Transform skewed variables into Gaussian
Convert numerical variables into discrete
Remove outliers from your variables
Extract meaningful features from dates and time variables
Learn techniques used in organisations worldwide and in data competitions
Increase your repertoire of techniques to preprocess data and build more powerful machine learning models
Requirements
A Python installation
Jupyter notebook installation
Python coding skills
Some experience with Numpy and Pandas
Familiarity with Machine Learning algorithms
Familiarity with Scikit-Learn
Description
NEW! Updated in November 2020 for the latest software versions, including use of new tools and open-source packages, and additional feature engineering techniques.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online . In this course, you will learn how to engineer features and build more powerful machine learning models.
Who is this course for?
So, you’ve made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you’re probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can’t find many consolidated resources about feature engineering. Maybe even just blogs? So you may start to wonder: how are things really done in tech companies?
This course will help you! This is the most comprehensive online course in variable engineering . You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.
File folder on MEGA
Key : XOQA-9hzFSrvRv1_w7yYHw
