Feature Selection for Machine Learning
What you'll learn
- Learn about filter, embedded and wrapper methods for feature selection
- Find out about hybdrid methods for feature selection
- Select features with Lasso and decision trees
- Implement different methods of feature selection with Python
- Learn why less (features) is more
- Reduce the feature space in a dataset
- Build simpler, faster and more reliable machine learning models
- Analyse and understand the selected features
- Discover feature selection techniques used in data science competitions
- A Python installation
- Jupyter notebook installation
- Python coding skills
- Some experience with Numpy and Pandas
- Familiarity with Machine Learning algorithms
- Familiarity with scikit-learn
Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online.
In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models.
Who is this course for?
You’ve given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables. At this stage you’ve probably realized that many data sets contain an enormous amount of features, and some of them are identical or very similar, some of them are not predictive at all, and for some others it is harder to say.
You wonder how you can go about to find the most predictive features. Which ones are OK to keep and which ones could you do without? You also wonder how to code the methods in a professional manner. Probably you did your online search and found out that there is not much around there about feature selection. So you 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 selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features.
What will you learn?
I have put together a fantastic collection of feature selection techniques, based on scientific articles, data science competitions and of course my own experience as a data scientist.
Specifically, you will learn:
How to remove features with low variance
How to identify redundant features
How to select features based on statistical tests
How to select features based on changes in model performance
How to find predictive features based on importance attributed by models
How to code procedures elegantly and in a professional manner
How to leverage the power of existing Python libraries for feature selection
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, Scikit-learn, pandas and mlxtend.
At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.
This comprehensive feature selection course includes about 70 lectures spanning ~8 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, I update the course regularly, to keep up with the Python libraries new releases and include new techniques when they appear.
So what are you waiting for? Enroll today, embrace the power of feature selection and build simpler, faster and more reliable machine 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
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.
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.