What you'll learn
- Understand different methods of feature selection
- Implement different methods of feature selection
- Reduce feature space in a dataset
- Build simpler, faster and more reliable machine learning models
- Analyse and understand the selected features
Course content
- Preview04:03
- Preview03:33
- Preview03:07
- 01:44Course Aim
- 01:00Optional: How to approach this course
- 02:01Course Material
- 00:15The code | Jupyter notebooks
- 00:04Presentations covered in this course
- 00:38Download the data sets
- 00:36FAQ: Data Science and Python programming
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 select features and build simpler, faster and more reliable machine learning models.
This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, 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, multiple methods that you can apply to select features from your data set.
The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features. Then it describes more complex techniques that select variables taking into account variable interaction, the feature importance and its interaction with the machine learning algorithm. Finally, it describes specific techniques used in data competitions and the industry.
The lectures include an explanation of the feature selection technique, the rationale to use it, and the advantages and limitations of the procedure. It also includes full code that you can take home and apply to your own data sets.
This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.
With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of variable selection. Throughout the course you will use python as your main language.
So what are you waiting for? Enrol today, learn how to select variables for machine learning, and build simpler, faster and more reliable learning models.
Who this course is for:
- Beginner Data Scientists who want to understand how to select variables for machine learning
- Intermediate Data Scientists who want to level up their experience in feature selection for machine learning
- Advanced Data Scientists who want to discover alternative methods for feature selection
- Software engineers and academics switching careers into data science
- Software engineers and academics stepping into data science
- Data analysts who want to level up their skills in data science
Featured review
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Instructor
- 4.5 Instructor Rating
- 4,653 Reviews
- 20,216 Students
- 5 Courses
Bio first in English and then in Spanish:
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. Soledad 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 enabling people to step into and excel in data science, that is why she mentors data scientists and speaks at data science meetings regularly, 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.