semvec/pypi-packages huggingface dataset:
davidshack4u 0.2.0
Biblioteca para consultar cursos de hack4u.

# Hack4u Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introduccion a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introduccion al hacking [55 horas]
- Python Ofensivo [35 horas]


## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso basico

### Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
	print(course)
```

### Obtener un curso por nombre

```python
from hack4u import search_course_by_name

course = search_course_by_name("Python Ofensivo")
print(course)
```

### Calcular la duracion total de los cursos

```python
from hack4u.utils import total_duration
print(f"Duracion total: {total_duration()} horas")
```
ice-estimation 1.0.0
A package for estimation techniques
Requires Python: >=3.8

# ICE Estimation

```
pip install ice-estimation
```

Supported Filters: 

* Sliding Innovation Filter
* Kalman Filter

```
from ice_estimation import filters

filters.sif(x, z, u, P, A, B, C, Q, R,delta)
filters.kf_filter(x, z, u, P, A, B, C, Q, R)
```
modules-packages-hack4u 0.1.0
Biblioteca para consultar cursos de hack4u.

#Hack4u Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.

##Cursos disponibles:

- Introduccion a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introduccion al hacking [55 horas]
- Python Ofensivo [35 horas]


## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso basico

### Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
	print(course)
```

### Obtener un curso por nombre

```python
from hack4u import search_course_by_name

course = search_course_by_name("Python Ofensivo")
print(course)
```

### Calcular la duracion total de los cursos

```python
from hack4u.utils import total_duration
print(f"Duracion total: {total_duration()} horas")
```
securedai 0.1.1
Secure your ML models using securedai

# SecuredAI Python Package
`securedai` is a Python package designed to easily implement security features for machine learning models or other applications. With simple commands, users can instantiate the package and quickly apply security layers to their models.

## Installation
You can install the `securedai` package using pip:
```bash
pip install securedai
```

## Quickstart
Once the package is installed, you can start using it immediately. Here's a simple example:
```python
from securedai import Secured

# Instantiate the Secured class
sc = Secured() 
# Console output: "Constructed secured instance"

# Apply security to 9 models
result = sc.implement(9)
# Console output: "Implemented securety for 9 models!"

print(result)
# Console output: {'status': True, 'message': 'Success'}
```

**Expected Output**:
```python
Constructed secured instance
Implemented security for 9 models!    
{'status': True, 'message': 'Success'}
```

## Features
- Easy Security Implementation: Apply security features to any number of models with one command.
- Clear Status Messages: Returns a success message with the status of the security implementation.

## Methods
`Secured.implement(model_count: int) -> dict`
- **Description**: Implements security for a specified number of models.
- **Parameters**:
    - `model_count (int)`: The number of models to apply security to.
- **Returns**:
    - A dictionary with `status` indicating success or failure, and a `message`.

## Contributing
Feel free to open issues or pull requests if you find any bugs or have feature requests.

## License
This project is licensed under the MIT License. See the LICENSE file for details.