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Learn with Me/Artificial Intelligence/AI Fundamentals

Current path

AI Fundamentals

Level: Beginner58 lessons

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Lesson index

01Introduction to Artificial Intelligence

What Is Artificial Intelligence?
AI vs. Traditional Programming
Narrow, General, and Superintelligent AI
How Machines Learn from Data
Essential AI Terms

02AI History and Evolution

Early Ideas and the Birth of AI
Symbolic AI and Expert Systems
The AI Winters
Where AI Stands Today
The Rise of Machine Learning
The Deep Learning Revolution
Transformers and the Generative Era

03How AI Systems Work

Data, Algorithms, and Models
The Model Training Pipeline
Training, Validation, and Test Data
Features, Parameters, and Patterns
Inference and Deployment
Evaluation and Overfitting

04Types of Machine Learning

Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Self-supervised Learning
Reinforcement Learning
Choosing a Learning Type

05Neural Networks and Deep Learning

What Is a Neural Network?
Layers and Activations
Loss and Optimization
Backpropagation, Simplified
Why Deep Learning Succeeded
Limits of Deep Learning

06Branches of Artificial Intelligence

AI Fields and Applications
Natural Language Processing
Branches of Artificial Intelligence
Computer Vision
Speech Recognition and Generation
Robotics and Autonomous Systems
Recommendation Systems
Multimodal AI

07Generative AI and Language Models

What Is Generative AI?
What Is a Large Language Model?
Tokens and Context Windows
Next-token Prediction
Transformers and Attention
Pretraining and Post-training
Hallucinations and Reliability

08Applications and Today’s AI

Assistants and AI Agents
AI in Education
AI in Healthcare
AI in Business and Marketing
AI in Software Development
Choosing an AI Use Case

09Ecosystem, Responsibility, and Future

Map of Leading AI Companies
Open and Closed Models
Chips, Cloud, and Data Centers
Responsible AI
Privacy, Copyright, and Bias
Jobs and the Future of AI
Conclusion and Your Next Learning Path

✓Path quiz — 10 questions

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Final quiz

Test your path knowledge

Answer 10 questions from the path lessons. You need 70% to pass.

1. Which description best defines artificial intelligence?

Systems that perform tasks usually requiring human abilities using models and algorithmsAny program running on a modern deviceA machine that necessarily possesses human consciousness

2. What is a key difference between machine learning and traditional programming?

The model learns patterns from data instead of requiring a separate rule for every caseMachine learning does not need dataTraditional programming does not use logic

3. Which AI type describes today's specialized systems?

Narrow AIArtificial general intelligenceSuperintelligence

4. What does a model develop while learning from data?

Patterns and parameters that help it predict new examplesOnly a literal copy of every fileConsciousness independent of the data

5. Why is test data kept separate from training data?

To measure performance on data the model did not learn fromTo increase the size of the training dataTo prevent the model from performing inference

6. What is a common sign of overfitting?

Strong training performance but weak performance on new dataSimilar strong performance on training and test dataThe model has no parameters

7. What distinguishes supervised learning?

Training on examples paired with correct answers or labelsLearning without dataRelying only on rewards

8. What primary signal does an agent learn from in reinforcement learning?

Rewards and penalties resulting from its actionsOnly correct labels for every exampleThe alphabetical order of files

9. What does a language model's context window mean?

The amount of tokens the model can consider within an interactionThe number of model usersThe physical size of the device screen

10. What is the best practice when using a generated answer for an important decision?

Verify facts and sources before relying on itTreat every answer as correct because it sounds confidentRemove the question context

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