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Explore our AI and future-skills courses
freeEDU offers a wide range of courses in AI, machine learning, data science, deep learning, cloud platforms, and applied technologies — all designed to help learners build real, practical skills.
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Learn AI from beginner to advanced level
Start with the basics and build all the way up to advanced AI and machine learning skills.
Year 1 Courses
Foundations
Introductory courses in AI, coding, data science, and essential tools.
Introduction to AI
Learn the basic concepts, ideas, and applications behind artificial intelligence.
TheoryDifficulty 1
Python Basics
Build essential Python programming skills for technical and AI-related learning.
CodingDifficulty 2
Prompt Engineering
Understand how to work effectively with AI systems through better prompting techniques.
TheoryDifficulty 2
ChatGPT
Explore how to use ChatGPT for productivity, learning, and real-world applications.
TheoryDifficulty 1
GitHub
Learn how to use GitHub for version control, collaboration, and code management.
TheoryDifficulty 1
MathCAD
Strengthen technical problem-solving with mathematical computing tools and concepts.
TheoryDifficulty 3
Python for Data Science
Apply Python to data analysis, workflows, and data-driven tasks.
CodingDifficulty 3
Math for Data Science
Build the mathematical foundation needed for data science and analytical thinking.
TheoryDifficulty 3
Data Preprocessing
Learn how to clean, prepare, and organize data for analysis and machine learning.
CodingDifficulty 3
Data Visualization with Python
Create clear and useful visualizations using Python tools and libraries.
CodingDifficulty 3
Data Visualization with R
Learn to present and explore data visually using R-based tools and workflows.
CodingDifficulty 3
Data Visualization with Tableau
Understand how to communicate data insights through interactive visual dashboards.
TheoryDifficulty 3
Year 2 Courses
Machine Learning
Courses focused on machine learning methods, tools, and practical applications.
SQL for Data Science
Learn how to work with structured data using SQL for analysis and data projects.
CodingDifficulty 3
Math for ML
Build the mathematical understanding needed for machine learning concepts and models.
TheoryDifficulty 4
ML Algorithms: Regression
Understand regression models and how they are used to make predictions from data.
CodingDifficulty 4
ML Algorithms: Classification
Learn how classification algorithms group and label data for decision-making tasks.
CodingDifficulty 4
ML Algorithms: Clustering
Explore unsupervised learning techniques used to discover patterns in data.
CodingDifficulty 4
ML Algorithms: Dimensionality Reduction
Learn techniques for simplifying complex data while preserving important information.
CodingDifficulty 5
Kaggle
Get familiar with data science competitions, datasets, and hands-on ML practice.
TheoryDifficulty 3
Gradio
Learn how to build simple interfaces for machine learning applications.
CodingDifficulty 3
TensorFlow
Develop practical skills using TensorFlow for machine learning model building.
CodingDifficulty 4
Applications of ML
See how machine learning is used across industries and real-world use cases.
TheoryDifficulty 3
Model Deployment
Understand how trained models are delivered and used in real applications.
TheoryDifficulty 4
Reinforcement Learning
Explore how AI systems learn through feedback, rewards, and sequential decisions.
CodingDifficulty 2
Year 3 Courses
Deep Learning and AI Systems
Advanced courses in deep learning, MLOps, robotics, and production-ready AI tools.
Scrum
Learn agile teamwork principles used in modern product and technology environments.
TheoryDifficulty 1
Math for Deep Learning
Strengthen your understanding of the math behind deep learning systems.
CodingDifficulty 5
Streamlit
Build simple and interactive data and AI apps with Streamlit.
CodingDifficulty 3
MLOps
Learn the workflows and systems needed to manage machine learning in production.
CodingDifficulty 3
MLFlow
Understand tools for experiment tracking, model management, and ML workflows.
CodingDifficulty 4
Hardware for ML Applications
Explore the hardware foundations that support machine learning performance and deployment.
TheoryDifficulty 2
Deep Learning with TensorFlow
Apply TensorFlow to deeper neural network models and advanced learning tasks.
CodingDifficulty 4
Artificial Intelligence for Robotics
Learn how AI methods are applied in robotics and intelligent systems.
TheoryDifficulty 2
DNN (Deep Neural Networks)
Explore the structure and use of deep neural networks in modern AI systems.
CodingDifficulty 4
CNN (Convolutional Neural Networks)
Learn how CNNs are used for image-based tasks and pattern recognition.
CodingDifficulty 4
ANN (Artificial Neural Networks)
Understand the principles and applications of artificial neural network models.
CodingDifficulty 4
RNN (Recurrent Neural Networks)
Learn how RNNs process sequential data in language, time-series, and related tasks.
CodingDifficulty 4
Year 4 Courses
Applied and Emerging AI
Courses focused on ethics, connected systems, and major cloud-based AI platforms.
AI Ethics
Understand the ethical questions, risks, and responsibilities that come with AI.
TheoryDifficulty 1
AI and the Internet of Things
Explore how AI works with connected devices and smart systems.
TheoryDifficulty 2
AWS ML
Learn about machine learning services and workflows in the AWS ecosystem.
TheoryDifficulty 4
Google Cloud Platform
Understand cloud-based tools and AI capabilities available through Google Cloud.
TheoryDifficulty 4
Azure ML
Explore machine learning tools, services, and deployment environments in Azure.
TheoryDifficulty 4
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