Coursera MOOC / Non-credit 0

Advanced Machine Learning with R: Apply & Predict

Name: Advanced Machine Learning with R: Apply & Predict
Price: 49 USD
Availability: InStock

About this course

Master advanced machine learning with R by learning how to build, evaluate, and interpret predictive models using a structured progression from statistical foundations to modern machine learning techniques. In this course, you will apply K-Means clustering, Naive Bayes classification, Support Vector Machines (SVM), Principal Component Analysis (PCA), neural network fundamentals, time series forecasting, gradient boosting, and market basket analysis through practical R programming examples. You will learn how to cluster unlabeled data, classify text and categorical data, construct document-term matrices, apply kernel methods for accurate classification, reduce dimensionality with PCA, interpret principal components, design foundational neural networks, and develop forecasting models using ARIMA and Prophet. You will also improve predictive performance with gradient boosting and uncover associations through market basket analysis while strengthening your ability to preprocess data, select appropriate algorithms, and interpret model results. Designed for data analysts, aspiring data scientists, and professionals seeking to expand their machine learning expertise with R, this course combines theory with hands-on implementation and real-world case studies. Its unique structure brings together unsupervised learning, supervised learning, dimensionality reduction, neural networks, forecasting, and association rule mining in one comprehensive learning experience. By the end of the course, you will be able to confidently apply advanced machine learning techniques in R to analyse data, build predictive models, and make data-driven decisions.

81/100

CourseAsk score

What the provider tells you: 45/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

apply K-Means clustering
perform Naive Bayes classification
execute Support Vector Machines (SVM)
implement Principal Component Analysis (PCA)
understand neural network fundamentals
develop time series forecasting models
utilize gradient boosting
conduct market basket analysis
preprocess data and select algorithms
interpret model results

Course objectives

build and evaluate predictive models
develop a strong understanding of advanced machine learning techniques
make data-driven decisions based on model outcomes

Machine Learning #machine learning #model evaluation #pca #data analysis #r programming #forecasting #neural networks #clustering #naive bayes #data preprocessing #time series #svm #gradient boosting #market basket analysis

$49.00

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