What is AI
Cognitive Ability and Problem Solving Capabaility --> Human Intelligence
Human Intelligence if replicated in machines --> Artificial General Intelligence(AGI)
AGI with specific and narrow objective --> AI
AI Terminology
ML --> Machine Learning
DL --> Deep Learning
DS --> Data Science
AI Domains
- Language Translation
- Speech Recognition
- Text to Speech
- Anamoly detection
- Product Recco
- Learn by Reward
- Weather Forecast
- Image from Text
Language AI Model
Recurrent Neural Network: process data sequentially and store hidden states
Long Short Term Memory: process data sequentially and retain context
Transformers: Process data in parallel. Self attention to better understand the context.
Audio Speech Model
Recurrent Neural Network
Long Short Term Memory
Transformers
Variational Autoencoders
Waveform Models
Siamese Network
Vision Image Model
convolutional neural networks
YOLO, which is You Only Look Once
Generative adversarial networks
Supervised Learning
Learning from Labeled Data
Classify data or make a prediction
Unsupervised Learning
Data has no outcome or label.
Discovering trends
Understand relationships with data sets
Retail Marketing and Sales
Fruits and Vegetable Nutrition
Reinforcement Learning
Trial and Error
Make decisions and choice
Deep Learning
Extracting features and rules from data.
Neural Network; Supervised Learning
Generative AI
Produce content.
Data Type
Numerical data: Measurable data
Categorical data: Characteristic(Nominal or Ordinal)
Time series data: Number Sequence
Text Data: Words and Paragraphs
Supervised Learning
- Spam Detection: Categorical Output, Binary in nature
- Disease Detection: Categorical Output, Binary in nature
- Sentiment analysis: Categorical Output, Multiclass in nature
Stock Price Prediction: Continous and quantitive
Data Access
Data Preparation
Modeling
Validation
Deployment
Monitor and Iteration
Regression --> Linear Regression
Independent Feature (Input) --> Dependent Feature (Output)
Metrics to evaluate Regression
Mean Absolute Error
Mean Squared Error
R Squared
Classification --> Logistics Regression
Metrics to Evaluate Classification
Accuracy: Ration of Correct Prediction/Total number of Prediction
Precision: True positive case/All Positive cases
Recall: Actual Positives identified correctly
Unsupervised Learning
Clustering
- Market Segmentation
- Outlier Analysis
- Reccomdetation System
Reinforcement Learning
- Agent
- Environment
- State
- Action
- Policy, comes from learning
Notes taken during learning: OCI AI Foundations Oracle University
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