AI Learnings in OCI
Tags: ai, learning, oci, oracle
Published: 2023-11-18 12:49:12

**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**

1. Language Translation
2. Speech Recognition
3. Text to Speech
4. Anamoly detection
5. Product Recco
6. Learn by Reward
7. Weather Forecast
8. 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



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/z7a9uv7ve13bzn2o3het.png)


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**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.

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**Data Type**

Numerical data: Measurable data
Categorical data: Characteristic(Nominal or Ordinal)
Time series data: Number Sequence
Text Data: Words and Paragraphs

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**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



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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


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hjpbovp5pxyiapy4iz1i.png)

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


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/aofbimseuadsncc3j9ec.png)

 **Reinforcement Learning**
- Agent
- Environment
- State
- Action
- Policy, comes from learning


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Notes taken during learning: OCI AI Foundations Oracle University