Find out more at Train in Data: https://www.trainindata.com/
Did you know that you can also use moving averages as input features?
If not, check out this blog to find out more, together with Python implementations:
www.blog.trainindata.com/master-movin...
Did you know that you can also use moving averages as input features?
If not, check out this blog to find out more, together with Python implementations:
www.blog.trainindata.com/master-movin...
We discuss 3 recent articles that have changed the conversation on resampling and SMOTE👇
www.trainindata.com/p/7-takes-on...
We discuss 3 recent articles that have changed the conversation on resampling and SMOTE👇
www.trainindata.com/p/7-takes-on...
If you are unhappy for whatever reason, we give you the money back.
That confident we are that you'll ❤️ our courses.
#trainindata
If you are unhappy for whatever reason, we give you the money back.
That confident we are that you'll ❤️ our courses.
#trainindata
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/bltkmoeitj
#machinelearning #datascience #jupyter #mlmodels #ML #mltools #notebooks #cloudplatforms
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https://f.mtr.cool/bltkmoeitj
#machinelearning #datascience #jupyter #mlmodels #ML #mltools #notebooks #cloudplatforms
ADASYN (Adaptive Synthetic Sampling) to the rescue! 🚀
Learn how it works + when to use it in our latest blog 👇
https://f.mtr.cool/rqstrumpnx
#MachineLearning #DataScience #ImbalancedData #ADASYN
ADASYN (Adaptive Synthetic Sampling) to the rescue! 🚀
Learn how it works + when to use it in our latest blog 👇
https://f.mtr.cool/rqstrumpnx
#MachineLearning #DataScience #ImbalancedData #ADASYN
Let this slide guide you through how it works.
#machinelearning #MICE #mlmodels #datascience #dataengineering #imputation #featureengineering
Let this slide guide you through how it works.
#machinelearning #MICE #mlmodels #datascience #dataengineering #imputation #featureengineering
In this article, Caruana, a prominent figure in machine learning and ensemble methods, tells us more about how they create ensembles from libraries of 1000s of machine learning models.
📄 https://f.mtr.cool/fpaqqnqxms
In this article, Caruana, a prominent figure in machine learning and ensemble methods, tells us more about how they create ensembles from libraries of 1000s of machine learning models.
📄 https://f.mtr.cool/fpaqqnqxms
🔍 You’ll:
✅ Group data (K-means, DBSCAN & more)
✅ Reduce complexity (PCA, UMAP)
✅ Work on real cases like RNA profiling
📍 https://f.mtr.cool/hdjiwbbsbl
🔍 You’ll:
✅ Group data (K-means, DBSCAN & more)
✅ Reduce complexity (PCA, UMAP)
✅ Work on real cases like RNA profiling
📍 https://f.mtr.cool/hdjiwbbsbl
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/svpfklfpda
#machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/svpfklfpda
#machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume
In this article, we break down essential evaluation metrics for classification models, starting with the Confusion Matrix. Perfect for anyone looking to build reliable #machinelearning systems!
Have a good read👇
In this article, we break down essential evaluation metrics for classification models, starting with the Confusion Matrix. Perfect for anyone looking to build reliable #machinelearning systems!
Have a good read👇
As of now, ELI5 has released a new version with full support for scikit-learn >1.6.0 and Python >3.10.
Check it out 👇
As of now, ELI5 has released a new version with full support for scikit-learn >1.6.0 and Python >3.10.
Check it out 👇
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
Learn how to group data (K-Means, DBSCAN, Louvain) + simplify it with PCA & UMAP, no prior experience needed!
Hands-on & practical 👇
👉 https://f.mtr.cool/zshxexbrds
#MachineLearning #DataScience
Learn how to group data (K-Means, DBSCAN, Louvain) + simplify it with PCA & UMAP, no prior experience needed!
Hands-on & practical 👇
👉 https://f.mtr.cool/zshxexbrds
#MachineLearning #DataScience
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/nozrfuruar
#machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/nozrfuruar
#machinelearning #datascience #CV #mlmodels #ML #MLCareer #MLresume
Automated hyperparameter optimization (HPO) streamlines the process. This paper reviews key techniques & tools for improving model accuracy & efficiency.
📃https://f.mtr.cool/wowjcrmwjg
Automated hyperparameter optimization (HPO) streamlines the process. This paper reviews key techniques & tools for improving model accuracy & efficiency.
📃https://f.mtr.cool/wowjcrmwjg
In this article, we explore when SMOTE is truly effective & why it’s remained popular.
Check it out!
https://f.mtr.cool/medbbpfril
In this article, we explore when SMOTE is truly effective & why it’s remained popular.
Check it out!
https://f.mtr.cool/medbbpfril
Learn to group data, reduce complexity with PCA & UMAP, and tackle real-world projects (no experience needed!)
🎓 Join us: https://f.mtr.cool/wlhxbboqkl
Learn to group data, reduce complexity with PCA & UMAP, and tackle real-world projects (no experience needed!)
🎓 Join us: https://f.mtr.cool/wlhxbboqkl
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/pinchbaedf
#machinelearning #datascience #smote #mlmodels #ML
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/pinchbaedf
#machinelearning #datascience #smote #mlmodels #ML
Learn their pros, cons, and best applications for both low and high-dimensional spaces!
What techniques do you use?
📽️
Learn their pros, cons, and best applications for both low and high-dimensional spaces!
What techniques do you use?
📽️
#python #machinelearning #MLModel #datascience #dataengineering
#python #machinelearning #MLModel #datascience #dataengineering
1. OneHotEncoder
2. OrdinalEncoder
3. TargetEncoder
When one-hot encoding gets too complex and ordinal encoding leads to inaccuracies, TargetEncoding often becomes the best choice. Learn more at the link below.
#targetencoder #ML
1. OneHotEncoder
2. OrdinalEncoder
3. TargetEncoder
When one-hot encoding gets too complex and ordinal encoding leads to inaccuracies, TargetEncoding often becomes the best choice. Learn more at the link below.
#targetencoder #ML
Learn to apply unsupervised ML in practice 👇
✅ K-Means, DBSCAN, HDBSCAN, Graph-based
✅ PCA & UMAP
✅ Real-world projects incl. RNA case study
Find out more : https://f.mtr.cool/cojxgkyhgq
Learn to apply unsupervised ML in practice 👇
✅ K-Means, DBSCAN, HDBSCAN, Graph-based
✅ PCA & UMAP
✅ Real-world projects incl. RNA case study
Find out more : https://f.mtr.cool/cojxgkyhgq
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/xefqrzzgeh
#machinelearning #datascience #imbalanceddata #undersampling #mlmodels #ML
Want to know more?
Click the link below to subscribe and stay tuned!👇
https://f.mtr.cool/xefqrzzgeh
#machinelearning #datascience #imbalanceddata #undersampling #mlmodels #ML
▶️ 90% of the time is spent on data preprocessing
▶️ 10% of the time is spent on model building, tuning and evaluation.
#machinelearning #ML #MLmodels #preprocessing #modelbuilding #datascience
▶️ 90% of the time is spent on data preprocessing
▶️ 10% of the time is spent on model building, tuning and evaluation.
#machinelearning #ML #MLmodels #preprocessing #modelbuilding #datascience