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Machine Learning for Marketing Analytics in R
In this course you'll learn how to use data science for several common marketing tasks.
Building Response Models in R
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
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.
MLOps for Business
Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.
Hyperparameter Tuning in R
Learn how to tune your model's hyperparameters to get the best predictive results.
Machine Translation with Keras
Are you curious about the inner workings of the models that are behind products like Google Translate?
Building Recommendation Engines with PySpark
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Machine Learning in the Tidyverse
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Support Vector Machines in R
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Performing Experiments in Python
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
Feature Engineering in R
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Generalized Linear Models in Python
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Designing Machine Learning Workflows in Python
Learn to build pipelines that stand the test of time.
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.
Deep Reinforcement Learning in Python
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Introduction to Linear Modeling in Python
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Practicing Machine Learning Interview Questions in Python
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Modeling with tidymodels in R
Learn to streamline your machine learning workflows with tidymodels.
Feature Engineering with PySpark
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
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.
Monitoring Machine Learning Concepts
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Recurrent Neural Networks (RNNs) for Language Modeling with Keras
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Building Recommendation Engines in Python
Learn to build recommendation engines in Python using machine learning techniques.