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

Access provided by PSGR Krishnammal College for Women

Get in-depth knowledge of a subject

from 18 reviews of courses in this program

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

from 18 reviews of courses in this program

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.

Details to know

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Taught in English

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Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from EDUCBA

Specialization - 6 course series

Machine Learning with R: Build, Analyze & Predict

Machine Learning with R: Build, Analyze & Predict

Course 1, 13 hours

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.

Advanced Machine Learning with R: Apply & Predict

Advanced Machine Learning with R: Apply & Predict

Course 2, 18 hours

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.

Linear Regression with R: Build & Optimize

Linear Regression with R: Build & Optimize

Course 3, 7 hours

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.

Machine Learning Projects in R with Caret

Machine Learning Projects in R with Caret

Course 4, 5 hours

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.

Machine Learning with Python & Statistics

Machine Learning with Python & Statistics

Course 5, 13 hours

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.

Machine Learning in Python: Analyze & Apply

Machine Learning in Python: Analyze & Apply

Course 6, 14 hours

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.

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Instructor

EDUCBA
EDUCBA
1,191 Courses282,488 learners

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EDUCBA

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