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

Included with Coursera Plus

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

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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

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

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

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

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

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

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

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Instructor

EDUCBA
EDUCBA
515 Courses126,695 learners

Offered by

EDUCBA

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