mlprac - Machine Learning Practice Notebooks

A comprehensive collection of Jupyter notebooks for practicing machine learning concepts with Python, NumPy, Pandas, and Scikit-learn.

Installation

Install the package using pip:

pip install mlprac

Usage

Download Notebooks

After installation, you can download all practice notebooks to your current working directory:

mlprac download

Or specify a custom destination:

mlprac download --dest my-notebooks

List Available Notebooks

To see all available notebooks without downloading:

mlprac download --list

Package Information

Get information about the package:

mlprac info

Notebook Contents

This package includes 30+ Jupyter notebooks covering:

• NumPy Basics (2.ipynb, 2a-2d.ipynb)

  • Array creation and manipulation
  • Indexing and slicing
  • Mathematical operations
  • Broadcasting

• Linear Regression (3.ipynb, 3a-3b.ipynb, 3multi.ipynb)

  • Simple linear regression
  • Multiple linear regression
  • Model evaluation

• Classification Algorithms (4.ipynb - 7.ipynb series)

  • Logistic regression
  • K-Nearest Neighbors (KNN)
  • Support Vector Machines (SVM)
  • Decision Trees

• Clustering (8.ipynb series)

  • K-Means clustering
  • Hierarchical clustering

• Neural Networks (9.ipynb series)

  • Basic neural network implementations
  • Deep learning concepts

• Advanced Topics (10.ipynb series)

  • Ensemble methods
  • Model optimization

Requirements

The notebooks use the following Python libraries:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn

Install them separately:

pip install numpy pandas matplotlib seaborn scikit-learn jupyter

Python API

You can also use the package programmatically in Python:

import mlprac

# Get the path to notebooks
notebooks_path = mlprac.get_notebooks_path()

# List all available notebooks
notebooks = mlprac.list_notebooks()
for nb in notebooks:
    print(nb)

License

MIT License