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Fraud Detection in R
Learn to detect fraud with analytics in R.
Predicting CTR with Machine Learning in Python
Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.
HR Analytics: Predicting Employee Churn in R
Predict employee turnover and design retention strategies.
Machine Learning for Marketing Analytics in R
In this course youll learn how to use data science for several common marketing tasks.
Hyperparameter Tuning in R
Learn how to tune your models hyperparameters to get the best predictive results.
MLOps for Business
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
Intermediate Predictive Analytics in Python
Learn how to prepare and organize your data for predictive analytics.
HR Analytics: Predicting Employee Churn in Python
In this course youll learn how to apply machine learning in the HR domain.
Introduction to Natural Language Processing in R
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Support Vector Machines in R
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Feature Engineering in R
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Sentiment Analysis in R
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Dimensionality Reduction in R
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Text Mining with Bag-of-Words in R
Learn the bag of words technique for text mining with R.
Data Privacy and Anonymization in Python
Learn to process sensitive information with privacy-preserving techniques.
Machine Learning in the Tidyverse
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Building Recommendation Engines with PySpark
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Case Study: School Budgeting with Machine Learning in Python
Learn how to build a model to automatically classify items in a school budget.
Designing Machine Learning Workflows in Python
Learn to build pipelines that stand the test of time.
Advanced NLP with spaCy
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Machine Learning for Marketing in Python
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Market Basket Analysis in Python
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Introduction to Data Versioning with DVC
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.