### Applied ML learning repo: 
[https://github.com/eugeneyan/applied-ml](https://github.com/eugeneyan/applied-ml)

### Steps machine learning deployment
- Data ingestion
- Data preparation
- Feature engineering
- Model training
- Model evaluation
- Model deployment
- Inference
- Model monitoring and re-training

### Regression
[Multiple Linear Regression Fish Weight Prediction | Kaggle](https://www.kaggle.com/satishgunjal/multiple-linear-regression-fish-weight-prediction)
##### Linear vs Logistic Regression
- The Linear regression models data using continuous numeric value. As against, logistic regression models the data in the binary values.
- Linear regression requires to establish the linear relationship among dependent and independent variable whereas it is not necessary for logistic regression.
- In the linear regression, the independent variable can be correlated with each other. On the contrary, in the logistic regression, the variable must not be correlated with each other.
Binomial distribution - models the outcome of n experiments with only 2 outcomes in each trial E.g. die rolled is >3, or heads/tails after each flip.
- has only 2 outcomes (1 or 0, success or failure)
Multinomial distribution - models the outcome of n experiments with >2 outcomes, and the outcomes can have a categorical distribution. E.g. rolling a die 5 times instead of 1, likelihood that company will report better than expected earnings.
- has 2 or more outcomes (binomial distri is a subset of multinomial distri when # of outcomes = 2)
- Each outcome will have it's own distribution
- probability of each outcome remains the same

### [[Recommendations Engine]]
### [[Supervised ML]]

