Created by Soledad GalliLast updated 10/2019EnglishEnglish Subs [Auto-generated]This course includes

- 9.5 hours on-demand video
- 18 articles
- 6 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Assignments

- Certificate of Completion

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

Course contentall 120 lectures 09:25:37Requirements

- 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 2019** 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.

**What will you learn?**

I have put together a fantastic collection of feature engineering techniques, based on scientific articles, white papers, data science competitions, and of course my own experience as a data scientist.

Specifically, you will learn:

- How to impute your missing data
- How to encode your categorical variables
- How to transform your numerical variables so they meet ML model assumptions
- How to convert your numerical variables into discrete intervals
- How to remove outliers
- How to handle date and time variables
- How to work with different time zones
- How to handle mixed variables which contain strings and numbers

Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an **elegant, efficient, and professional manner**, using Python, NumPy, Scikit-learn, pandas and a special open-source package that I created especially for this course: Feature- engine.

At the end of the course, you will be able to implement all your feature engineering steps in a single and elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency.

**Want to know more? Read on…**

In this course, you will initially become acquainted with the most widely used techniques for variable engineering, followed by more advanced and tailored techniques, which capture information while encoding or transforming your variables. You will also find detailed explanations of the various techniques, their advantages, limitations and underlying assumptions and the best programming practices to implement them in Python.

This comprehensive feature engineering course includes over 100 lectures spanning about 10 hours of video, and **ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects**.

REMEMBER, the course comes with a 30-day money back guarantee, so you can sign up today with no risk. **So what are you waiting for? Enrol today, embrace the power of feature engineering and build better machine learning models.**Who this course is for:

- Data Scientists who want to get started in pre-processing datasets to build machine learning models
- Data Scientists who want to learn more techniques for feature engineering for machine learning
- Data Scientist who want to limprove their coding skills and best programming practices for feature engineering
- Software engineers, mathematicians and academics switching careers into data science
- Data Scientists who want to try different feature engineering techniques on data competitions
- Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering

**Size: 3.76 GB**