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Feature Engineering for Machine Learning

Link : Feature Engineering for Machine Learning

Learn how to engineer features and build more powerful machine learning models. This is the most comprehensive, yet easy to follow, course for feature ...HIGHEST RATED

Feature Engineering for Machine Learning
4.6 (615 ratings)
3,972 students enrolled
Created by Soledad Galli

What you'll 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
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, the advantages and limitations, and the assumptions the technique makes on the data. It also includes full code that you can then take on and apply to your own data sets.

This course is suitable for complete beginners in data science looking to learn their first steps into data pre-processing, as well as for intermediate and 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. The course includes several techniques for missing data imputation, categorical variable encoding, numerical variable transformation and discretisation, as well as how to extract useful features from date and time variables. Throughout the course we use python as our main language, and open source packages for feature engineering, including the package "Feature Engine" which was specifically designed for this course.

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.

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

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