Build a practical foundation in machine learning with Python and R, from preparing data to evaluating predictive models.
Description
Welcome to Machine Learning A-Z™—a hands-on introduction to core machine-learning concepts, workflows, and practical applications.
This course helps you understand how data can be used to build models that identify patterns and make predictions. You will explore supervised and unsupervised learning with clear examples using Python and R.
- Machine learning foundations – Learn the difference between artificial intelligence, machine learning, data science, and traditional programming.
- Data preparation – Understand how to organise, clean, explore, and prepare data before training a model.
- Models and evaluation – Explore common algorithms, evaluate model performance, and interpret results responsibly.
What you will learn
- Understand the main types of machine learning
- Prepare and explore datasets for analysis
- Use Python and R for basic data-analysis tasks
- Learn the principles of regression models
- Understand classification and prediction models
- Explore clustering and unsupervised learning
- Train, test, and evaluate a machine-learning model
- Understand accuracy, errors, and model performance
- Recognise overfitting and improve model reliability
- Build a foundation for further AI and data-science projects
Requirements
- No previous machine-learning experience is required.
- Basic knowledge of Python or R is useful but not essential.
- A computer with internet access and curiosity about data and technology.
Who this course is for
- Beginners interested in machine learning and data science
- Students exploring technology and analytics careers
- Professionals who want to understand data-driven decision-making
- Developers who want to begin working with machine-learning models
