Machine Learning
Showing 25–36 of 116 results
Designing Machine Learning Workflows in Python
Learn to build pipelines that stand the test of time.
Developing Machine Learning Models for Production
Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
Dimensionality Reduction in Python
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Dimensionality Reduction in R
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
End-to-End Machine Learning
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Ensemble Methods in Machine Learning
Explore bagging, boosting, stacking, and more in this introduction to ensemble methods in machine learning.
Ensemble Methods in Python
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Feature Engineering
Machine learning is only as good as its training data. Learn how to process data properly before training your models.
Feature Engineering for Machine Learning in Python
Create new features to improve the performance of your Machine Learning models.
Feature Engineering for NLP in Python
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Feature Engineering in R
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.