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Engineering
LifestyleFeature Engineering for Machine Learning (Udemy.com)
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
Created by: Soledad Galli
Last updated March 2025
What you will 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 Description
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
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
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Instructor Details
- 4.6 Rating
3,826 Reviews
Soledad Galli
Soledad Galli is a Lead Data Science in Insurance and Finance. Soledad helped Finance and Insurance companies build Machine Learning models to assess Credit Risk and to prevent Fraud. Her work as a data scientist led Soledad to be the winner of the Data Leaders awards 2018 for data science and analytics.
Soledad Galli has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions.
Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, has taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities.
Soledad regularly shares Data Science and Machine Learning knowledge as a speaker in meetings and conferences for the Data Science Community, and through blogs, articles and online courses. She mentors students and professionals who want to step into and excel in Data Science.
Sole has recently created Train In Data, with the mission to facilitate and empower people and organisations worldwide step into and excel in data science and analytics.
Soledad is passionate about extracting information from data and transforming it into a meaningful story, helping Data Scientist step into
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Reviews
By Raphael Fernandes Reis Roriz on 10/26/2020
Muito bom o curso, alm de ensinar tcnicas mais avanadas de Feature Engineering a instrutora cobre tambm, de forma concisa, alguns tpicos menos avanados.
By Samaresh Kumar Pradhan on 10/26/2020
Amazing start and course content. I am very much exited to explore the scientific techniques and many more..
By Jian Wu on 10/24/2020
Clearly structured
By Jordanka Marceta on 10/23/2020
The material in this course is organized in a coherent manner and well presented. The description of techniques is complete and exhaustive with plenty of additional materials and external resources.
I do give it a 4-star because the course misses mini-projects/ exercises and quizzes to accompany lessons. Having those would make it more practical, interactive and engaging.
By Marcello Henrique Rodrigues de Oliveira on 10/21/2020
She presents a fair explanation on various feature engineering techniques and a library of her own authorship. This course was very useful to me.
By Matthew Gregg on 10/10/2020
I didn't know what to expect but this course is nothing short of hard work that I know will help in the future. It can be and will be challenging in the beginning but with a instructor that you MUST pay attention too bc she has an accent(subtitles ON)! she will guide to the top of your game. Two thumbs up.
By Carlisson Miller on 10/8/2020
Excelente.
By Sahil Gupta on 10/6/2020
@Soledad, you are an amazing teacher. The course was exactly what I was looking for. The content of the course was above my expectations.
A very big Thank You to the amazing teacher @Soledad
By Gabriel dos Santos Gonalves on 10/5/2020
One more great course from Soledad Galli! The quality of the content presented and structure of the course makes it pleasant to follow every class. Soledad teaches you how to deal with real world data problems to help you extract the most from your features. I highly recommend it to anyone working with ML and Data Science.
By Rafael Laluz on 10/4/2020
Very clear to follow.
By Attila Zabos on 9/22/2020
Awesome. The best course I've seen so far on pre-processing data - i.e feature engineering. There are a lot of courses about AI and ML algorithms but most of them either briefly mention feature engineering, or skip this step completely and assume that all the data is in a nice and usable form. This course fills the gap and clearly explains what needs to be considered and done to raw data in order for the ML algorithms to work properly.
By Yen Bui on 9/19/2020
The course is great, very well documented and easy to understand although it covers very intensive feature engineering techniques. However, it would be great if it can be more interactive with learners by letting learners coding along with author and explain each line of code, what it does, and parameters meanings. Also challenge learners to think and research to complete some small challenges so they can sharpen their coding skill & problem resolving.
Quality Score
Overall Score : 92 / 100
- Video Duration:
13.4h - Price: $84.99
- Provider:
Udemy - Updated:
March 2025 - Level:
Intermediate - Language:
English - Certificate:
Yes
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