- 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
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- 17 More Must-Know Data Science Interview Questions and Answers, Part 2 - Feb 22, 2017.
The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.
- Stacking Models for Improved Predictions - Feb 21, 2017.
This post presents an example of regression model stacking, and proceeds by using XGBoost, Neural Networks, and Support Vector Regression to predict house prices.
- Random Forests in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
- Data Science Basics: An Introduction to Ensemble Learners - Nov 8, 2016.
New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.
- Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results - Feb 12, 2016.
Get a handle on ensemble methods from voting and weighting to stacking and boosting, with this well-written overview that includes numerous Python-style pseudocode examples for reinforcement.
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- 5 Tribes of Machine Learning – Questions and Answers - Nov 27, 2015.
Leading researcher Pedro Domingos answers questions on 5 tribes of Machine Learning, Master Algorithm, No Free Lunch Theorem, Unsupervised Learning, Ensemble methods, 360-degree recommender, and more.
- Are you trying to acquire Machine Learning Skills? - Sep 16, 2015.
Embarking on a journey through the lands of machine learning? Here are few important lessons like Feature Engineering, Model tuning, Overfitting, Ensembling etc. which you should keep in mind along the way.
- KDnuggets™ News 15:n21, Jul 1: Top 20 R packages; Using Ensembles in Kaggle; Tutorials and How-Tos - Jul 1, 2015.
Top 20 R packages by popularity; Tutorials, Overviews, How-Tos; Open Source Enabled Interactive Analytics; Using Ensembles in Kaggle Data Science Competitions.
- PAW Chicago: Five Unbeatable Analytics Workshops - May 19, 2015.
Take your predictive analytics up a notch with unbeatable PAW Chicago workshops, covering R, predictive modeling, ensemble methods and more.
- Advanced Data Analytics for Business Leaders Explained - Sep 24, 2014.
A business-level explanation of most important data analytics and machine learning methods, including neural networks, deep learning, clustering, ensemble methods, SVM, and when do use what models.
- Top KDnuggets tweets, Aug 4-5: Ensemble Methods, a brief history; Data Scientist role shifting - Aug 6, 2014.
Ensemble Methods are the backbone of #MachineLearning - a brief history; Data Scientist role shifting, with companies focusing on Developers; To add #MachineLearning for Python, scikit-learn; for Hadoop: Mahout; Meet Fortune 2014 #BigData All-Stars: data scientists, entrepreneurs, CEOs.
- Top KDnuggets tweets, Apr 9-10: MLlib: Scalable Machine Learning on Spark; Ensemble methods overview - Apr 11, 2014.
MLlib: Scalable Machine Learning on Spark (free ebook); Ensemble methods usually give best results in Machine Learning - an overview; Prediction.io open source machine learning server ; Maslow Hierarchy of Analytical Needs - too clever?