Building AI – Free Artificial Intelligence Course by University of Helsinki

Building AI is a free, self-paced online artificial intelligence course from the University of Helsinki's Elements of AI initiative. It introduces the algorithms, mathematical ideas, and practical techniques that power modern AI systems.

The course welcomes beginners, students, working professionals, business owners, and aspiring AI developers. You can study the fundamental concepts without programming or choose more challenging Python exercises to strengthen your technical skills.

What You Will Learn

1. Understand how artificial intelligence solves real-world problems.
2. Explore optimization and search algorithms.
3. Apply probability and Bayesian reasoning.
4. Understand supervised machine learning and prediction.
5. Explore text processing and natural language processing.
6. Understand logistic regression, neural networks, and deep learning.
7. Develop an idea for a practical AI application.

Complete Course Curriculum

Chapter 1: Getting Started with AI

Explore the importance of artificial intelligence and the fundamental techniques used to solve optimization problems.

1. 1.1 Why AI Matters: Understand the relevance, opportunities, and practical applications of artificial intelligence.
2. 1.2 Optimization: Learn how algorithms search for effective solutions among different possibilities.
3. 1.3 Hill Climbing: Explore iterative optimization and its strengths and limitations.

Chapter 2: Dealing with Uncertainty

Learn how AI systems make decisions when information is incomplete or uncertain.

1. 2.1 Probability Fundamentals: Understand probability concepts and uncertainty.
2. 2.2 The Bayes Rule: Explore conditional probability and how new evidence updates predictions.
3. 2.3 Naive Bayes Classifier: Understand probabilistic classification techniques.

Chapter 3: Machine Learning

Discover how machine learning systems use data to recognize patterns and make predictions.

1. 3.1 Linear Regression: Explore mathematical models for predicting numerical values.
2. 3.2 The Nearest Neighbor Method: Learn how similarity between data points supports prediction.
3. 3.3 Working with Text: Explore natural language processing and numerical representations of text.
4. 3.4 Overfitting: Understand why machine learning models may perform poorly on new data.

Chapter 4: Neural Networks

Understand the foundational concepts behind artificial neural networks and deep learning.

1. 4.1 Logistic Regression: Explore a mathematical approach to classification.
2. 4.2 From Logistic Regression to Neural Networks: Understand how neural network structures extend simpler prediction models.
3. 4.3 Deep Learning: Explore the principles and applications of deeper neural networks.

Chapter 5: Conclusion and AI Project

Review your learning and explore how AI methods can be applied to practical challenges.

1. 5.1 Summary: Review the main algorithms, techniques, and concepts covered throughout the course.
2. 5.2 Your AI Idea: Formulate a realistic idea for solving a problem with artificial intelligence.
3. 5.3 AI Idea Gallery: Explore ideas shared through the course community.

Learning Format and Difficulty Levels

Building AI offers three flexible exercise levels:

1. Beginner: Multiple-choice exercises that require no programming experience.
2. Intermediate: Exercises involving basic Python code modification and simple programming tasks.
3. Advanced: More demanding Python programming exercises for learners with prior coding experience.

Students can switch between exercise levels according to their knowledge and confidence. Programming exercises run in a browser-based environment on desktop computers.

Exercises and Assessment

The course contains 21 exercises across its chapters. To successfully complete the course, learners must finish at least 19 exercises and answer at least 50% correctly. Higher-level exercise completion can qualify learners for intermediate or advanced honors.

Course Duration

The official estimated workload is approximately 50 hours. Actual learning time depends on the selected difficulty level, prior knowledge, and participation in optional project activities. The course is self-paced, allowing learners to study according to their own schedules.

Prerequisites

There are no mandatory prerequisites for beginners. Learners choosing intermediate or advanced exercises should have some familiarity with Python programming.

Certificate and Pricing

The course content and exercises are free to access. Learners who successfully complete the course may purchase an optional shareable electronic certificate for EUR 50, subject to the provider's current terms.

Eligible learners completing qualifying intermediate or advanced requirements may also apply for University of Helsinki academic credits under the institution's applicable rules.

Who Should Enroll?

1. Beginners exploring artificial intelligence.
2. Students interested in machine learning.
3. Professionals preparing to work on AI projects.
4. Business owners exploring practical AI applications.
5. Python learners looking to understand AI algorithms.
6. Technology enthusiasts seeking a structured introduction to AI methods.

Frequently Asked Questions

Is the Building AI course free?

Yes. Course access and exercises are free. The optional electronic certificate requires an additional payment.

Do I need Python programming knowledge?

No programming experience is necessary for beginner-level exercises. Intermediate and advanced exercises require Python knowledge.

How long does Building AI take?

The provider estimates approximately 50 hours, depending on your chosen exercises and learning pace.

Does Building AI offer a certificate?

Yes. An optional shareable electronic certificate is available after successful completion and payment of the applicable certificate fee.

Is Building AI suitable for beginners?

Yes. The beginner exercises introduce important AI concepts without requiring coding experience.

Start Learning Building AI

Learn the fundamentals of machine learning, optimization, probability, and neural networks through an accessible online course.

Official Course Website: https://buildingai.elementsofai.com/