🤖150 essential AI terms📊

 150 essential AI terms -Machine Learning, Natural Language Processing (NLP), Predictive Analytics, and Chatbots.

Machine Learning (ML) – 50 Terms

1. Machine Learning (ML): Algorithms that enable computers to learn from data and improve over time without explicit programming. Wikipedia

2. Supervised Learning: Training models on labeled datasets to predict outcomes.

3. Unsupervised Learning: Identifying patterns in unlabeled data.

4. Reinforcement Learning: Learning optimal actions through rewards and penalties.

5. Overfitting: Model performs well on training data but poorly on new data.

6. Underfitting: Model is too simple to capture underlying patterns.

7. Cross-Validation: Technique to assess model performance on unseen data.

8. Bias: Error due to overly simplistic assumptions in the learning algorithm. Wikipedia

9. Variance: Error due to model's sensitivity to small fluctuations in the training set.

10. Regularization: Technique to prevent overfitting by adding a penalty term to the loss function.

11. Gradient Descent: Optimization algorithm to minimize the loss function.

12. Learning Rate: Step size in gradient descent optimization.Google for Developers+8Wikipedia+8Wikipedia+8

13. Epoch: One complete pass through the training dataset.

14. Batch Size: Number of training examples used in one iteration.

15. Loss Function: Measures the difference between predicted and actual values.

16. Activation Function: Function applied to neurons in neural networks to introduce non-linearity.

17. Neural Network: Computational model inspired by the human brain's network of neurons.

18. Deep Learning: Subset of ML involving neural networks with multiple layers.

19. Convolutional Neural Network (CNN): Specialized neural network for processing grid-like data, such as images.

20. Recurrent Neural Network (RNN): Neural network designed for sequential data.

21. Long Short-Term Memory (LSTM): Type of RNN capable of learning long-term dependencies.

22. Autoencoder: Neural network used for unsupervised learning of efficient codings.

23. Support Vector Machine (SVM): Supervised learning model for classification and regression tasks.

24. Decision Tree: Model that splits data into branches to make predictions.

25. Random Forest: Ensemble of decision trees to improve predictive performance.

26. K-Nearest Neighbors (KNN): Algorithm that classifies data based on the closest training examples.

27. K-Means Clustering: Unsupervised algorithm that partitions data into K clusters.

28. Principal Component Analysis (PCA): Dimensionality reduction technique.

29. Feature Engineering: Process of selecting and transforming variables for model training.

30. Feature Selection: Identifying the most relevant variables for model building.

31. Hyperparameter Tuning: Process of optimizing model parameters.

32. Grid Search: Exhaustive search over specified parameter values.

33. Random Search: Randomly sampling parameter combinations for optimization.

34. Model Evaluation: Assessing the performance of a trained model.

35. Confusion Matrix: Table used to describe the performance of a classification model.

36. Precision: Proportion of true positives among all positive predictions.

37. Recall: Proportion of true positives among all actual positives.

38. F1 Score: Harmonic mean of precision and recall.

39. ROC Curve: Graph showing the performance of a classification model at all thresholds.

40. AUC (Area Under Curve): Measure of the ability of a classifier to distinguish between classes.

41. Ensemble Learning: Combining multiple models to improve performance.

42. Bagging: Ensemble method that trains multiple models in parallel.

43. Boosting: Ensemble method that trains models sequentially.

44. AdaBoost: Boosting algorithm that combines weak learners into a strong one.

45. Gradient Boosting: Boosting technique that builds models sequentially to correct errors.

46. XGBoost: Efficient and scalable implementation of gradient boosting.

47. LightGBM: Gradient boosting framework that uses tree-based learning algorithms.

48. CatBoost: Gradient boosting algorithm that handles categorical features well.

49. Model Deployment: Process of integrating a trained model into a production environment.

50. Model Monitoring: Tracking model performance over time to detect issues.

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🗣️ Natural Language Processing (NLP) – 50 Terms

51. Natural Language Processing (NLP): Field of AI focused on the interaction between computers and human language.Ithaca College

52. Tokenization: Breaking text into individual words or phrases.Wikipedia+1Financial Times+1

53. Stemming: Reducing words to their root form.

54. Lemmatization: Reducing words to their base or dictionary form.

55. Part-of-Speech Tagging: Identifying grammatical parts of speech in text.

56. Named Entity Recognition (NER): Identifying and classifying entities in text.

57. Sentiment Analysis: Determining the emotional tone behind a body of text.

58. Stop Words: Common words filtered out before processing text.

59. Bag-of-Words (BoW): Text representation model that counts word occurrences.

60. TF-IDF: Statistical measure to evaluate the importance of a word in a document.

61. Word Embeddings: Vector representations of words capturing semantic meaning. Wikipedia

62. Word2Vec: Model that learns word associations from a large corpus of text.

63. GloVe: Global Vectors for Word Representation.

64. FastText: Word embedding model that considers subword information.

65. Language Modeling: Predicting the next word in a sequence.

66. N-grams: Contiguous sequences of n items from a given text.

67. Syntax Parsing: Analyzing the grammatical structure of a sentence.

68. Dependency Parsing: Analyzing the dependencies between words in a sentence.

69. Coreference Resolution: Determining when different words refer to the same entity.

70. Topic Modeling: Discovering abstract topics within a collection of documents.

71. Latent Dirichlet Allocation (LDA): Generative statistical model for topic modeling.

72. Text Classification: Assigning categories to text.

73. Text Summarization: Creating a concise version of a longer text.

74. Machine Translation: Automatically translating text from one language to another.

75. BLEU Score: Metric for evaluating the quality of machine-translated text.

76. Perplexity: Measurement of how well a probability model predicts a sample.

77. Transformer: Model architecture that uses self-attention mechanisms.

78. BERT: Bidirectional Encoder Representations from Transformers.

📊 Predictive Analytics – essential Terms

1. Predictive Analytics: Using data, statistical algorithms, and ML to forecast future outcomes.

2. Forecasting: Predicting future values based on historical data trends.

3. Regression Analysis: Estimating the relationship between variables.

4. Logistic Regression: Used for predicting categorical outcomes (e.g., yes/no).

5. Time Series Analysis: Analyzing data points collected or recorded at time intervals.

6. Anomaly Detection: Identifying unusual patterns that do not conform to expected behavior.

7. Churn Prediction: Predicting which customers are likely to stop using a service.

8. Customer Lifetime Value (CLV): Predicting the total revenue from a customer during their relationship with a business.

9. Uplift Modeling: Predicting the incremental impact of a specific action (like a campaign).

10. Classification: Predicting discrete labels (e.g., spam or not spam).

11. Regression Tree: A decision tree used for regression tasks.

12. Mean Absolute Error (MAE): Average of absolute errors between predicted and actual values.

13. Root Mean Squared Error (RMSE): Standard deviation of prediction errors.

14. R-squared (R²): A metric showing how well the model fits the data.

15. Data Preprocessing: Cleaning and preparing raw data for analysis.

16. Feature Importance: Determining which variables have the biggest influence on predictions.

17. Data Splitting: Dividing data into training, validation, and test sets.

18. Outliers: Unusual data points that can skew predictions.

19. Data Imputation: Filling in missing values in datasets.

20. Scenario Modeling: Predicting different outcomes based on variable changes.

21. Business Intelligence (BI): Using data analysis tools to support business decision-making.

22. Monte Carlo Simulation: Running many simulations to predict probable outcomes.

23. What-If Analysis: Exploring different scenarios by changing input values.

24. Scorecard Modeling: Ranking items (e.g., customers) based on predictive scores.

25. Risk Modeling: Assessing the likelihood of future adverse events (e.g., loan default).

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🤖 Chatbots – essential  Terms

1. Chatbot: AI tool that simulates conversation with users.

2. Conversational AI: Technologies that allow machines to understand and respond to human language.

3. Rule-Based Chatbot: Responds based on pre-defined rules and flows.

4. AI-Powered Chatbot: Uses machine learning and NLP to understand and respond intelligently.

5. Intent Recognition: Identifying what the user wants to do.

6. Entity Recognition: Extracting relevant data from user input (e.g., dates, names).

7. Dialog Flow: The structured conversation path a chatbot follows.

8. Context Management: Remembering what the user has said during the conversation.

9. Fallback Intent: Response given when the chatbot doesn't understand the user input.

10. Multimodal Chatbot: Uses voice, text, or visuals in conversations.

11. Omnichannel Chatbot: Available on multiple platforms (e.g., web, WhatsApp, Facebook).

12. Proactive Chatbot: Initiates conversations instead of waiting for input.

13. Voice Bot: Chatbot that uses speech instead of text.

14. Bot Training: Teaching the chatbot how to respond using training data.

15. Utterances: Different ways users can phrase the same intent.

16. Human Handoff: Transferring the conversation from bot to a human agent.

17. NLP Engine: The backend engine that interprets user inputs.

18. TTS (Text-to-Speech): Converts written text into spoken words.

19. STT (Speech-to-Text): Converts spoken input into written text.

20. Conversation Analytics: Insights gathered from bot conversations.

21. API Integration: Connecting chatbot with external systems (like CRMs or databases).

22. Chatbot Metrics: KPIs like user engagement, resolution rate, and fallback rate.

23. Sentiment Detection: Identifying emotional tone of user inputs.

24. Bot Persona: The chatbot’s personality, tone, and style.

25. Flow Builder: Tool used to visually design chatbot conversation logic.

Sources

Wikipedia  Google