Feature Engineering for Machine Learning
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 content
- Preview05:16
- Preview06:00
- Preview03:08
- 01:09How to approach this course
- 01:27Setting up your computer
- 01:59Course Material
- 00:15Download Jupyter notebooks
- 01:23Download datasets
- 00:04Download course presentations
- 02:14Moving Forward
- 00:42FAQ: Data Science, Python programming, datasets, presentations and more...
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
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 probably 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 only 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.
In addition, the code is updated regularly to keep up with new trends and new Python library releases.
So what are you waiting for? Enroll 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 improve 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
Featured review
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Instructor
- 4.6 Instructor Rating
- 6,372 Reviews
- 27,239 Students
- 7 Courses
Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science.
As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations.
Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning.
Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics.
Sole 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, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities.
Feel free to contact her on LinkedIn.
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Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos.
Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones.
A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina.
Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos.
Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones.
Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades.
No dudes en contactarla en LinkedIn.