EDUCBA
AI Machine Learning with R & Python Projects Specialization
EDUCBA

AI Machine Learning with R & Python Projects Specialization

Master Machine Learning with R and Python. Gain hands-on experience building ML models in R and Python through real-world projects.

EDUCBA

Instructor: EDUCBA

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Get in-depth knowledge of a subject
Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply machine learning algorithms in R and Python to analyze and predict real-world data.

  • Optimize, validate, and interpret models using statistical and computational techniques.

  • Build end-to-end ML projects, from preprocessing to deployment-ready solutions.

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Taught in English
Recently updated!

October 2025

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Specialization - 6 course series

What you'll learn

  • Apply ML foundations, probability, and statistical concepts in R.

  • Implement regression, classification, and decision tree models.

  • Use ensemble methods like random forests and boosting in R.

Skills you'll gain

Category: Regression Analysis
Category: R Programming
Category: Decision Tree Learning
Category: Predictive Modeling
Category: Random Forest Algorithm
Category: Probability Distribution
Category: Statistical Analysis
Category: Applied Machine Learning
Category: Data Analysis
Category: Machine Learning
Category: Statistical Modeling
Category: Data Manipulation
Category: Exploratory Data Analysis
Category: Supervised Learning
Category: Statistical Methods

What you'll learn

  • Apply clustering, Naive Bayes, PCA, and neural networks in R.

  • Forecast time series with ARIMA, Prophet, and boosting methods.

  • Implement market basket analysis and optimize predictive models.

Skills you'll gain

Category: R Programming
Category: Machine Learning
Category: Supervised Learning
Category: Dimensionality Reduction
Category: Predictive Modeling
Category: Text Mining
Category: Time Series Analysis and Forecasting
Category: Artificial Neural Networks
Category: Unsupervised Learning
Category: Forecasting
Category: Data Mining
Category: Exploratory Data Analysis
Category: Probability & Statistics
Category: Applied Machine Learning

What you'll learn

  • Define regression concepts and build simple/multiple models in R.

  • Apply dummy variables, statistical tests, and model validation.

  • Optimize models with backward elimination for predictive accuracy.

Skills you'll gain

Category: Regression Analysis
Category: Predictive Modeling
Category: Statistical Hypothesis Testing
Category: Data Validation
Category: Statistical Methods
Category: Data Analysis
Category: Feature Engineering
Category: Supervised Learning
Category: Statistical Modeling
Category: R Programming
Category: Data Visualization

What you'll learn

  • Prepare datasets, handle missing values, and apply imputation.

  • Perform correlation analysis and manage data imbalance.

  • Implement clustering with caret and validate ML workflows.

Skills you'll gain

Category: Data Processing
Category: Unsupervised Learning
Category: Data Cleansing
Category: R Programming
Category: Correlation Analysis
Category: Data Quality
Category: Data Validation
Category: Exploratory Data Analysis
Category: Machine Learning Algorithms
Category: Data Manipulation
Category: Applied Machine Learning
Category: Feature Engineering
Category: Data Integrity
Category: Statistical Analysis
Category: Machine Learning
Category: Analysis

What you'll learn

  • Apply probability, sampling, and distributions to datasets.

  • Use linear algebra and hypothesis testing for data analysis.

  • Build and validate ML models with Python in real-world contexts.

Skills you'll gain

Category: Probability
Category: Statistics
Category: Statistical Inference
Category: Probability Distribution
Category: Data Mining
Category: Statistical Hypothesis Testing
Category: Python Programming
Category: Machine Learning
Category: Sampling (Statistics)
Category: Linear Algebra
Category: Statistical Analysis
Category: Data Analysis
Category: Machine Learning Algorithms

What you'll learn

  • Apply NumPy, Pandas, and Matplotlib for data analysis & visualization.

  • Build, train, and validate supervised & unsupervised ML models.

  • Implement NLP, face recognition, and text classification projects.

Skills you'll gain

Category: NumPy
Category: Applied Machine Learning
Category: Unsupervised Learning
Category: Natural Language Processing
Category: Scikit Learn (Machine Learning Library)
Category: Matplotlib
Category: Text Mining
Category: Pandas (Python Package)
Category: Supervised Learning
Category: Machine Learning
Category: Feature Engineering
Category: Data Manipulation
Category: Python Programming
Category: Data Visualization
Category: Performance Tuning

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Instructor

EDUCBA
EDUCBA
557 Courses146,901 learners

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EDUCBA

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