Discover and read the best of Twitter Threads about #Pycaret
Most recents (6)
90% of data scientists don't track their experiments.
Here's how to fix that with one line of code... 🧵
#datascience
Here's how to fix that with one line of code... 🧵
#datascience
Experiments are the lifeblood of a data scientist.
They help you answer questions like:
Which model performed best?
Which model is actively deployed?
When is the last time we updated the model?
But 90% of data scientist are just saving a model file and thinking their finished
They help you answer questions like:
Which model performed best?
Which model is actively deployed?
When is the last time we updated the model?
But 90% of data scientist are just saving a model file and thinking their finished
BIG ANNOUNCEMENT: I'm beyond excited to announce that in 5 days, I'm launching my brand new course- The #Python for Machine Learning & API's Course.
This course will transform your #career.
Here's what's inside... 🧵
#datascience #course
This course will transform your #career.
Here's what's inside... 🧵
#datascience #course
This launch marks the culmination of 2 years of research...
It covers The 6 Top #Python libraries for machine learning and production:
It covers The 6 Top #Python libraries for machine learning and production:
Over the past 3 years, I've been writing Python code daily.
And this coming Thursday, I'm excited to share what I've learned. 🧵
#datascience #career #python #rstats
And this coming Thursday, I'm excited to share what I've learned. 🧵
#datascience #career #python #rstats
Most of you know me as an #R guy. And I 1000% love R. ❤️
But I've also grown over the past 10+ years that I've been practicing data science as a data scientist, as a consultant, and as an educator.
But I've also grown over the past 10+ years that I've been practicing data science as a data scientist, as a consultant, and as an educator.
What I've come to realize is that no one language is perfect.
Each has its strengths. And weaknesses.
And more often than not we get caught up in debating things that **don't** really matter...
Like R vs Python.
Each has its strengths. And weaknesses.
And more often than not we get caught up in debating things that **don't** really matter...
Like R vs Python.
I continue to be impressed by the ease of doing machine learning with Pycaret.
This is especially great for #R people that want to learn #Python.
Let me explain...
#DataScience #Rstats
This is especially great for #R people that want to learn #Python.
Let me explain...
#DataScience #Rstats
As many of you know, my primary data science toolkit is #R. ❤️
I've been doing data science in production at @bizScienc and developing open-source R software for over 10 years.
- Modeltime (+4 ecosystem pkgs)
- Timetk
- Tidyquant
- CorrelationFunnel
I've been doing data science in production at @bizScienc and developing open-source R software for over 10 years.
- Modeltime (+4 ecosystem pkgs)
- Timetk
- Tidyquant
- CorrelationFunnel
But, I interact with team members and interface with clients...
and their language of choice is often Python.
It's refreshing that I can quickly switch to Python when needed, and not need to write 5000 lines of #ScikitLearn code to do basic machine learning.
and their language of choice is often Python.
It's refreshing that I can quickly switch to Python when needed, and not need to write 5000 lines of #ScikitLearn code to do basic machine learning.
Connaissez-vous #pyCaret ?
Si ce n'est pas le cas, je vous conseille vivement d'y jeter un oeil
Cette bibliothèque vous permet de tester facilement les performances des principaux algorithmes de Machine Learning
illustration >
Si ce n'est pas le cas, je vous conseille vivement d'y jeter un oeil
Cette bibliothèque vous permet de tester facilement les performances des principaux algorithmes de Machine Learning
illustration >
Test sur la classification du dataset IRIS
on obtient ces résultats :
[ML tools & tips] 🧵
Have you ever used sklearn's pipeline class to enhance your analysis?
While not mandatory, pipelines bring important benefits if implemented in our code base.
Here is a short thread of why you should use pipelines to improve your #machinelearning work 👇
Have you ever used sklearn's pipeline class to enhance your analysis?
While not mandatory, pipelines bring important benefits if implemented in our code base.
Here is a short thread of why you should use pipelines to improve your #machinelearning work 👇
In #datascience and machine learning, a pipeline is a set of sequential steps that allows us to control the flow of data.
They are very useful as they make our code cleaner, more scalable and readable. They are used to organize the various phases of a project.
They are very useful as they make our code cleaner, more scalable and readable. They are used to organize the various phases of a project.
Implementing pipelines is not mandatory but has significant advantages, such as
- cleaner code
- less room for error
- implemented like a typical model with .fit()
- cleaner code
- less room for error
- implemented like a typical model with .fit()
