Master the essential skills to land a job as a machine learning scientist! You'll augment your R programming skillset with the toolbox to perform supervised and unsupervised learning. You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.
Overview
Syllabus
- Supervised Learning in R: Classification
- In this course you will learn the basics of machine learning for classification.
- Supervised Learning in R: Regression
- In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
- Feature Engineering in R
- Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
- Unsupervised Learning in R
- This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
- Machine Learning in the Tidyverse
- Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
- Intermediate Regression in R
- Learn to perform linear and logistic regression with multiple explanatory variables.
- Cluster Analysis in R
- Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
- Machine Learning with caret in R
- This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
- Modeling with tidymodels in R
- Learn to streamline your machine learning workflows with tidymodels.
- Machine Learning with Tree-Based Models in R
- Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
- Dimensionality Reduction in R
- Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
- Support Vector Machines in R
- This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
- Fundamentals of Bayesian Data Analysis in R
- Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
- Hyperparameter Tuning in R
- Learn how to tune your model's hyperparameters to get the best predictive results.
- Bayesian Regression Modeling with rstanarm
- Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
- Predicting Movie Rental Durations
- Introduction to Spark with sparklyr in R
- Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Taught by
Zachary Deane-Mayer, Max Kuhn, Hank Roark, Brett Lantz, Richie Cotton, John Mount, Nina Zumel, Rasmus Bååth, Dmitriy Gorenshteyn, Jake Thompson, Shirin Elsinghorst (formerly Glander), Kailash Awati, David Svancer, Sandro Raabe, Matt Pickard, and Jorge Zazueta
