This Specialization equips learners with a strong foundation in machine learning, combining the statistical power of R with the flexibility of Python. Learners will progress from regression and classification to clustering, neural networks, and time series forecasting, while also mastering advanced preprocessing and model optimization. With a balance of theory and applied coding, participants will gain the ability to analyze, predict, and deploy machine learning models effectively. Designed for students, professionals, and aspiring data scientists, this program ensures learners can apply their knowledge to real-world scenarios with confidence.
AI Machine Learning with R & Python Projects Specialization
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.
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
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.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
October 2025
71 practice exercises
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
Statistical Analysis, Machine Learning, Random Forest Algorithm, Data Analysis, R Programming, Supervised Learning, Applied Machine Learning, Predictive Modeling, Probability Distribution, Exploratory Data Analysis, Statistical Modeling, Data Manipulation, Decision Tree Learning, Regression Analysis, and 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
Dimensionality Reduction, Exploratory Data Analysis, Forecasting, Unsupervised Learning, Text Mining, R Programming, Applied Machine Learning, Machine Learning, Time Series Analysis and Forecasting, Supervised Learning, Predictive Modeling, Probability & Statistics, Artificial Neural Networks, and 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
Data Validation, Supervised Learning, R Programming, Regression Analysis, Statistical Methods, Predictive Modeling, Statistical Modeling, Statistical Hypothesis Testing, Feature Engineering, Data Visualization, and Data Analysis
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
Correlation Analysis, Applied Machine Learning, Unsupervised Learning, Data Quality, Analysis, Data Validation, Statistical Analysis, Machine Learning, Data Cleansing, Feature Engineering, Data Manipulation, R Programming, Machine Learning Algorithms, Data Processing, Data Integrity, and Exploratory Data 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
Linear Algebra, Python Programming, Probability, Machine Learning Algorithms, Statistical Hypothesis Testing, Statistical Inference, Sampling (Statistics), Data Mining, Statistical Analysis, Data Analysis, Probability Distribution, Statistics, and Machine Learning
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
Natural Language Processing, Data Manipulation, Machine Learning, Text Mining, Performance Tuning, Matplotlib, Scikit Learn (Machine Learning Library), Applied Machine Learning, Supervised Learning, Unsupervised Learning, Pandas (Python Package), Python Programming, Feature Engineering, NumPy, and Data Visualization
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
The Specialization is designed to be completed in approximately 20 to 21 weeks, with a recommended study commitment of 3–4 hours per week. This flexible pacing allows learners to balance professional or academic responsibilities while steadily progressing through both foundational and advanced concepts. By the end of the program, participants will have invested focused time not only in mastering theoretical frameworks but also in applying their knowledge through hands-on projects, ensuring practical, career-ready expertise in machine learning with R and Python.
Learners are expected to have a basic understanding of statistics, probability, and linear algebra, along with familiarity in either R or Python programming. No advanced machine learning experience is required, as the Specialization builds skills step by step.
Yes, the courses are structured in a progressive sequence. Beginning with foundational concepts in R, learners then advance to specialized techniques, regression analysis, project-based applications, and Python machine learning. Following the order ensures a smooth learning journey from fundamentals to advanced applications.
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