gobbezlearningtoolbox 0.3.0

My learning toolbox, with every code that i have studied

PYPI PYTHON STUDY RESOURCES

This is a simple idea of grouping my notes and study material in a single space, thus creating a PyPi module where everyone can access these. Also, the print-text is colored!

Installation and Use

Install the module from PIP:

pip install gobbezlearningtoolbox

Check all the materials in the module:

from gobbezlearningtoolbox.listall import ListAll
print(ListAll())

Use the material you want to study and find all the contents (example code):

from gobbezlearningtoolbox.datascience import DataScience

ds = DataScience()
print(ds.help())

Pick a content and start studying (example code):

from gobbezlearningtoolbox.datascience import DataScience

ds = DataScience()
print(ds.machinelearning())

List of materials:

DataScience:

Methods and most common functions for Data Science, DataFrame manipulation and Machine Learning.

• imports_dataframe():

• imports_machinelearning():

• eda():

• operations():

DeepLearning:

Most common Keras Deep Learning models.

• imports_regression(): Most used imports for Keras Deep Learning - Regression

• imports_classifier(): Most used import for Keras Deep Learning - Classifier

• sequential(): Simple ready-to-go guide for a sequential Deep Learning model using Keras

• image_classifier(): Simple model to classify images using CNN

• dropout(): Explain how to set a simple DropOut

• learning_rate_scheduler(): Explain how to modify learning rate based on epochs

• mixed_precision(): Explain how to setup a mixed_precision training

• gradient_accumulation(): Explain how to use gradient accolumation to increase batch size

• compile(): Explain how to compile a model

• train(): Explain how to train a model

• train_with_gradient_accumulation(): Explain advanced techniques for training a model

• predict_regression(): Explain how to make regression predictions with the model

• predict_classification(): Explain how to make classification predictions with the model

• evaluate(): Explain how to evaluate the model

• plot(): Explain how to plot results of the model

TelegramBot:

Simple quick-start guide for the python-telegram-bot module to create your own Telegram Bot.

• imports(): Find the most common imports to start

• quick_start(): Quick setup explanations

Contribute

You can contribute to the GitHub Repository or use it locally:

git clone https://github.com/gobbez/gobbezlearningtoolbox.git

Notes

Please note that this module isn't complete, and it only gives a basic entry-level to start. For more information contact me. You can find the PyPi module here: https://pypi.org/project/gobbezlearningtoolbox

Updates

• added telegrambot (18/03/2025)
• added listall (15/03/2025)
• added deeplearning (15/03/2025)
• added datascience (15/03/2025)