Machine Learning Scientist with R

4.5 /5

(4 Ratings)

Offered byDataCamp

Machine Learning Scientist with R
at
DataCamp
Overview

Master the essential skills to land a job as a machine learning scientist

Duration	57 hours
Mode of learning	Online
Schedule type	Self paced
Credential	Certificate

Machine Learning Scientist with R

Table of contents

Overview
Course Details
Curriculum
Faculty
Student Reviews

Machine Learning Scientist with R
at
DataCamp
Course details

Skills you will learn

Spark R Statistics Data analysis

What are the course deliverables?

Supervised Learning in R: Classification
Supervised Learning in R: Regression
Unsupervised Learning in R
Machine Learning in the Tidyverse
Intermediate Regression in R
Cluster Analysis in R

More about this course

Learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance
Get an introduction to Bayesian statistics, natural language processing, and Spark
Learn the basics of machine learning for classification
Learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models
Learn to perform linear and logistic regression with multiple explanatory variables
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 Scientist with R
at
DataCamp
Curriculum

Supervised Learning in R: Classification

Supervised Learning in R: Regression

Unsupervised Learning in R

Machine Learning in the Tidyverse

Intermediate Regression in R

Cluster Analysis in R

Machine Learning with caret in R

Modeling with tidymodels in R

Machine Learning with Tree-Based Models in R

Model Development with R

Support Vector Machines in R

Fundamentals of Bayesian Data Analysis in R

Topic Modeling in R

Hyperparameter Tuning in R

Bayesian Regression Modeling with rstanarm

Machine Learning Scientist with R
at
DataCamp
Faculty details

Brett Lantz

Brett Lantz is a data scientist at the University of Michigan and the author of Machine Learning with R. After training as a sociologist, Brett has applied his endless thirst for data to projects that involve understanding and predicting human behavior.

John Mount

John is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. He is the author of several R packages, including the data treatment package vtreat.

Nina Zumel

Nina is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. She is co-author of the popular text Practical Data Science with R and occasionally blogs at the Win-Vector Blog on data science and R.

Hank Roark

Hank is a Senior Data Scientist at Boeing and a long time user of the R language. Prior to his current role, he led the Customer Data Science team at H2O.ai, a leading provider of machine learning and predictive analytics services.