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Practical Machine Learning

A hands-on course that empowers learners to build real-world machine learning models using R’s caret package.

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

What will you in the Practical Machine Learning Course

  • Learn the full workflow of predictive modeling, from data preprocessing to final model evaluation.

  • Understand critical concepts like overfitting, cross-validation, and out-of-sample error.

  • Apply machine learning algorithms including decision trees, random forests, and regularized regression.

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  • Use the caret package in R for building, training, and validating models.

  • Learn how to combine multiple models and use unsupervised methods for prediction.

Program Overview

1. Introduction to Prediction and Study Design
Duration: ~2 hours

  • Overview of predictive modeling concepts.

  • Introduction to training/test sets, error types, and cross-validation.

  • Basics of designing a machine learning study.

2. Machine Learning with caret in R
Duration: ~2 hours

  • Working with the caret package to train and evaluate models.

  • Data splitting, preprocessing (scaling, PCA), and model tuning.

  • Plotting predictors and using caret’s modeling workflow.

3. Decision Trees, Random Forests, and Boosting
Duration: ~1.5 hours

  • Understanding and implementing tree-based models.

  • Random forests and boosting explained with practical examples.

  • Introduction to model-based prediction approaches.

4. Regularization and Model Combination
Duration: ~2 hours

  • Concepts of regularized regression (e.g., ridge and lasso).

  • Combining multiple predictive models to improve accuracy.

  • Brief introduction to forecasting and unsupervised prediction.

  • Final assignment: build a working prediction model and submit for peer review.

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Job Outlook

  • Data Scientists: Gain practical experience in model building and validation.

  • Machine Learning Engineers: Learn foundational methods for scalable ML applications.

  • Business Analysts: Use data-driven techniques to support strategic decision-making.

  • Academic Researchers: Apply machine learning methods to experimental or observational data.

  • R Programmers: Advance your skills in applying machine learning using the caret package.

9.7Expert Score
Highly Recommended
A robust course that delivers strong hands-on experience in supervised learning using real datasets and widely-used R libraries.
Value
9.3
Price
9.5
Skills
9.7
Information
9.6
PROS
  • Strong focus on practical machine learning concepts
  • Teaches a powerful and flexible R package (caret)
  • Prepares learners to build and test models end-to-end
  • Offers a capstone-style prediction project
CONS
  • Requires prior R and basic statistics knowledge
  • Limited theory on advanced models or deep learning

Specification: Practical Machine Learning

access

Lifetime

level

Beginner

certificate

Certificate of completion

language

English

Course | Career Focused Learning Platform
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