Showing 30 question(s)
Answer:
Artificial Intelligence (AI) is a branch of computer science that enables machines to simulate human intelligence, including learning, reasoning, problem-solving, and decision-making.
Code Example:
# AI Example
print("Artificial Intelligence")Answer:
AI is the broad field of creating intelligent systems. Machine Learning is a subset of AI where systems learn from data. Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers.
Code Example:
AI
└── Machine Learning
└── Deep LearningAnswer:
Machine Learning is a branch of AI that enables computers to learn from data and improve predictions without being explicitly programmed.
Code Example:
from sklearn.linear_model import LinearRegression
model = LinearRegression()Answer:
Deep Learning is a subset of Machine Learning that uses artificial neural networks with multiple hidden layers to solve complex problems such as image recognition and language processing.
Code Example:
from tensorflow import keras
model = keras.Sequential()Answer:
An Artificial Neural Network is a computational model inspired by the human brain. It consists of interconnected neurons organized into input, hidden, and output layers.
Code Example:
Input Layer
↓
Hidden Layers
↓
Output LayerAnswer:
Supervised learning is a Machine Learning technique where models are trained using labeled datasets to predict outputs for new inputs.
Code Example:
Examples:
✔ Spam Detection
✔ House Price Prediction
✔ Image ClassificationAnswer:
Unsupervised learning finds hidden patterns or relationships in unlabeled data. Common techniques include clustering and dimensionality reduction.
Code Example:
Examples:
✔ Customer Segmentation
✔ Market Basket AnalysisAnswer:
Natural Language Processing (NLP) enables computers to understand, process, and generate human language for applications such as chatbots, translation, and sentiment analysis.
Code Example:
Applications:
• Chatbots
• Machine Translation
• Text Summarization
• Sentiment AnalysisAnswer:
Generative AI is a type of AI that creates new content such as text, images, videos, audio, and code based on prompts or existing data.
Code Example:
Examples:
• ChatGPT
• Gemini
• Claude
• DALL·EAnswer:
A Large Language Model (LLM) is an AI model trained on massive amounts of text data to understand and generate human-like language. Examples include GPT, Gemini, Claude, and Llama.
Code Example:
Prompt
↓
Large Language Model
↓
Generated ResponseAnswer:
Reinforcement Learning is a type of Machine Learning where an agent learns by interacting with an environment and receiving rewards or penalties for its actions.
Code Example:
Agent → Action → Environment
↑ ↓
Reward ← StateAnswer:
Overfitting occurs when a model learns the training data too well, including noise, resulting in poor performance on unseen data.
Code Example:
Training Accuracy: 99%
Testing Accuracy: 72%
Model is OverfittedAnswer:
Underfitting occurs when a model is too simple to learn the underlying patterns in the training data.
Code Example:
Training Accuracy: 60%
Testing Accuracy: 58%
Model is UnderfittedAnswer:
A training dataset is the portion of data used to train a machine learning model to recognize patterns.
Code Example:
Dataset
├── Training (80%)
└── Testing (20%)Answer:
A test dataset is used to evaluate the performance of a trained machine learning model on unseen data.
Code Example:
model.predict(testData)Answer:
Accuracy is the percentage of correct predictions made by a model out of all predictions.
Code Example:
Accuracy =
Correct Predictions
-------------------
Total PredictionsAnswer:
Precision measures how many predicted positives are actually positive, while Recall measures how many actual positives were correctly identified.
Code Example:
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)Answer:
F1 Score is the harmonic mean of Precision and Recall. It is useful when the dataset is imbalanced.
Code Example:
F1 = 2 ×
(Precision × Recall)
--------------------
Precision + RecallAnswer:
An activation function determines whether a neuron should be activated. Common activation functions include ReLU, Sigmoid, and Tanh.
Code Example:
ReLU(x) = max(0, x)Answer:
Backpropagation is an algorithm used to train neural networks by calculating gradients and updating weights using gradient descent.
Code Example:
Forward Pass
↓
Calculate Error
↓
Backpropagation
↓
Update WeightsAnswer:
Gradient Descent is an optimization algorithm used to minimize a model’s loss function by updating weights iteratively.
Code Example:
Weight =
Weight - LearningRate × GradientAnswer:
Computer Vision is a field of AI that enables machines to interpret and analyze images and videos.
Code Example:
Applications
• Face Recognition
• Object Detection
• OCR
• Medical ImagingAnswer:
Tokenization is the process of splitting text into smaller units called tokens, such as words or subwords.
Code Example:
"I love AI"
↓
["I", "love", "AI"]Answer:
Stemming reduces words to their root form by removing prefixes or suffixes.
Code Example:
Playing → Play
Running → RunAnswer:
Lemmatization converts words to their meaningful dictionary base form using linguistic analysis.
Code Example:
Better → Good
Running → RunAnswer:
Prompt Engineering is the practice of designing effective prompts to obtain accurate and relevant responses from AI models.
Code Example:
Prompt:
Explain AI in simple terms.
↓
Clear ResponseAnswer:
Fine-tuning is the process of training a pre-trained model on a specific dataset to improve performance for a particular task.
Code Example:
Pre-trained Model
↓
Custom Dataset
↓
Fine-tuned ModelAnswer:
RAG combines information retrieval with Large Language Models to generate responses using external knowledge sources.
Code Example:
User Query
↓
Retriever
↓
Relevant Documents
↓
LLM
↓
AnswerAnswer:
AI bias occurs when a model produces unfair or prejudiced results due to biased training data or flawed algorithms.
Code Example:
Biased Data
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Biased Model
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Unfair PredictionsAnswer:
AI ethics ensures AI systems are fair, transparent, accountable, secure, and respect user privacy while minimizing harmful outcomes.
Code Example:
✔ Fairness
✔ Transparency
✔ Privacy
✔ Accountability