๐ค AI Fundamentals You Should Know
AI is becoming an important skill across almost every industry. You don't need to become an AI researcher to understand it, but you should know the fundamentals.
๐ 1. What is Artificial Intelligence?
AI is the field of creating systems that can perform tasks that typically require human intelligence.
Examples:
Understanding language
Recognizing images
Making predictions
Solving problems
Making decisions
๐ 2. AI vs Machine Learning vs Deep Learning
Think of them as levels:
Artificial Intelligence
โ
Machine Learning
โ
Deep Learning
AI โ Broad field of intelligent systems
ML โ Systems learn patterns from data
DL โ ML using multi-layer neural networks
๐ 3. Types of Machine Learning
Everyone working with AI should know:
โข Supervised Learning
โข Unsupervised Learning
โข Reinforcement Learning
The key difference is how the model learns.
๐ 4. What is Training?
Training is the process of teaching a model using data.
The model identifies patterns in the training data and adjusts its parameters to improve its predictions.
๐ 5. What is Inference?
Inference happens when a trained model receives new data and produces a prediction or output.
Training โ Learn
Inference โ Predict
๐ 6. What is a Dataset?
A dataset is a collection of data used to train, validate, or test an AI model.
It can contain:
โข Features
โข Labels
โข Numerical data
โข Categorical data
โข Text
โข Images
โข Audio
โข Video
๐ 7. What are Features and Labels?
Features are the inputs used by a model.
Label/Target is what the model is trying to predict.
Example:
Age + Income + Credit Score
โ
Loan Approval
The first three are features, while loan approval is the target.
๐ 8. What is Overfitting?
Overfitting occurs when a model learns the training data too closely, including noise, and performs poorly on new data.
Too simple โ Underfitting
Good balance โ Generalization
Too complex โ Overfitting
๐ 9. What is a Neural Network?
A neural network is a computational model made up of interconnected nodes called neurons.
It typically contains:
Input Layer
โ
Hidden Layers
โ
Output Layer
Neural networks are the foundation of many modern AI systems.
๐ 10. What are Transformers?
Transformers are a neural network architecture that uses attention mechanisms to process relationships between elements in data.
They power many modern AI systems, especially:
โข LLMs
โข Translation systems
โข Text generation
โข Multimodal AI
๐ 11. What is an LLM?
A Large Language Model (LLM) is a model trained on large amounts of text to understand and generate language.
LLMs can perform tasks such as:
Question answering
Summarization
Translation
Coding
Content generation
๐ 12. What are Embeddings?
Embeddings convert information such as text into numerical vectors that capture semantic relationships.
Similar concepts tend to have similar vector representations.
They are widely used in:
Semantic search
RAG
Recommendation systems
Clustering
๐ 13. What is RAG?
RAG stands for Retrieval-Augmented Generation.
4August 21, 2026 392 6
