Feature Engineering for Machine Learning

IT & Software

Feature Engineering for Machine Learning

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Feature Engineering for Machine Learning

From beginner to advanced
4.5 (185 ratings)

1,376 students enrolled
Created by Soledad Galli
Last updated 5/2018
 English
 English [Auto-generated]

Preview this course

What Will I Learn?
  • Pre-process variables that contain missing data
  • Capture information from the missing values in your data

  • Work successfully with categorical variables

  • Convert labels of categorical variables into numbers that capture insight
  • Manipulate and transform numerical variables to extract the most predictive power
  • Transform date variables into insightful features
  • Apply different techniques of variable transformation to make features more predictive
  • Confidently clean and transform data sets for successful machine learning model building
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

Learn how to engineer features and build more powerful machine learning models.

This is the most comprehensive, yet easy to follow, course for feature engineering available online. Throughout this course you will learn a variety of techniques used worldwide for data cleaning and feature transformation, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist.


You will have at your fingertips, altogether in one place, a variety of techniques that you can apply to capture as much insight as you possibly can with the features of your data set.

The course starts describing the most simple and widely used methods for feature engineering, and then describes more advanced and innovative techniques that automatically capture insight from your variables. It includes an explanation of the feature engineering technique, the rationale to use it, and the pros, cons and assumptions it makes. It also includes full code that you can then take on and apply to your own data sets.

This course is therefore suitable for complete beginners in data science looking to learn their first steps into data pre-processing, as well as for intermediate and even advanced data scientists seeking to level up their skills.

With more than 50 lectures and 10 hours of video this comprehensive course covers every aspect of variable transformation. Throughout the course we use python as our main language.

This course comes with a 30 day money back guarantee. In the unlikely event you don’t find this course useful, you’ll get your money back, no questions asked.

So what are you waiting for? Enrol today, embrace the power of feature engineering and build better machine learning models.

Who is the target audience?
  • Beginner Data Scientists who want to get started in pre-processing datasets to build machine learning models
  • Intermediate Data Scientists who want to level up their experience in feature engineering for machine learning
  • Advanced Data Scientists who want to discover new and innovative techniques for feature engineering
  • Software engineers, mathematicians and academics switching careers into data science
  • Software engineers, mathematicians and academics stepping into data science

 

Feature Engineering for Machine Learning.zip   (download)
1.35 GB
http://k511.me/7fh/Feature_Engineering_for_Machine_Learning.zip

 


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