Intermediate
14h
Data Science with R: Decision Trees and Random Forests
Gain insights into essential machine learning algorithms in R, including CART, random forest, and XGBoost. Discover model tuning and cross-validation to create accurate, robust data science models.
The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners looking to master industry algorithms in the field of data science.
In this course, you’ll learn about several essential algorithms used in machine learning, including classification and regression trees (CART), random forest, and XGBoost. CART is a decision tree algorithm that’s used for both classification and regression problems. Random forest is an ensemble learning method that uses multiple decision trees to improve the accuracy of predictions. XGBoost, short for Extreme Gradient Boosting, is a powerful algorithm that’s also used for regression and classification problems. You’ll also learn about cross-validation and model tuning, which are essential skills for crafting valuable machine learning models.
After taking this course, you’ll have the crucial skills to ensure that the machine learning models you create are accurate, robust, and reliable.
The R programming language is widely used in the field of data science. Machine learning is a fundamental skill for learners loo...Show More
WHAT YOU'LL LEARN
An understanding of the basics of machine learning and supervised learning
Familiarity with the differences between classification and regression trees
A working knowledge of XGBoost algorithm
Familiarity with random forest
An understanding of the basics of machine learning and supervised learning
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TAKEAWAY SKILLS
Content
1.
Welcome to the Course
4 Lessons
Get familiar with machine learning fundamentals, key datasets, and predictive analytics in R.
2.
Supervised Learning
7 Lessons
Unpack the core of supervised learning, decision trees, overfitting, and model tuning in machine learning.
3.
Classification Tree Math
6 Lessons
Examine key mathematical concepts and techniques for optimizing classification tree splits using Gini impurity.
4.
Using Classification Trees in R
7 Lessons
Apply your skills to conduct EDA, prepare data, specify algorithms, and fit classification trees in R.
5.
Introducing the Bias-Variance Tradeoff
3 Lessons
Take a look at understanding model complexity and the bias-variance tradeoff in machine learning.
6.
Model Tuning
4 Lessons
Tackle cross-validation, optimal model tuning, decision tree complexity, and pruning to enhance model performance.
7.
Model Tuning with tidymodels
5 Lessons
Piece together the parts of model accuracy, cross-validation, hyperparameter tuning, and visualization with tidymodels.
8.
Feature Engineering
6 Lessons
Step through transforming raw data into meaningful features, avoiding information leakage, creating decision boundaries, and handling missing data.
9.
Regression Trees
5 Lessons
Walk through regression trees, SSE, and training with tidymodels for numeric predictions.
10.
The Random Forest Algorithm
4 Lessons
Break apart the random forest's ensemble, bagging, and feature randomization techniques.
11.
Using Random Forests
7 Lessons
Break down the steps to train, tune, and evaluate random forest models using R.
12.
Gradient Boosting Trees
6 Lessons
Map out the steps for implementing and tuning Gradient Boosting Trees using XGBoost in R.
13.
Continuing Your Journey
1 Lessons
Focus on hands-on practice, engaging in projects, and expanding skills to unsupervised learning.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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