1
Q
Artificial Intelligence
A
2
Q
Machine Learning
A
3
Q
Deep Learning
A
4
Q
Supervised Learning
A
5
Q
Unsupervised Learning
A
6
Q
Reinforcement Learning
A
7
Q
Training Data
A
8
Q
Validation Data
A
9
Q
Test Data
A
10
Q
Features (Data Concepts)
A
11
Q
Labels (Data Concepts)
A
12
Q
Ground Truth (Data Concepts)
A
13
Q
Synthetic Data
A
14
Q
Model Training & Evaluation
A
15
Q
Data Augmentation
A
16
Q
Model
A
17
Q
Inference
A
18
Q
Training
A
19
Q
Overfitting
A
20
Q
Underfitting
A
21
Q
Transfer Learning
A
22
Q
Model Evaluation
A
23
Q
Benchmark
A
24
Q
Classification (Problem Types)
A
25
Regression (Problem Types)
26
Clustering (Problem Types)
27
Anomaly Detection (Problem Types)
28
Confusion Matrix (Evaluation Metrics)
29
Precision (Evaluation Metrics)
30
Recall (Evaluation Metrics)
31
F1 S
32
Accuracy (Evaluation Metrics)
33
Mean Absolute Error (MAE) (Evaluation Metrics)
34
Root Mean Square Error (Evaluation Metrics)
35
Computer Vision
36
Object Detection
37
Image Classification
38
Facial Recognition
39
Optimal Character Recognition
40
Image Segmentation
41
Natural Language Processing
42
Natural Language Understanding
43
Natural Language Generation
44
Sentiment Analysis
45
Named Entity Recognition
46
Part of Speech Tagging
47
Summarization
48
Question Answering
49
Data Lake
50
ETL (Extract, Transform, Load)
51
Feature Store
AWS AI Practitioner
flashcards
Decks in class (5)
# Cards
