# Machine-Learning-with-Python

**Repository Path**: mirrors_lepy/Machine-Learning-with-Python

## Basic Information

- **Project Name**: Machine-Learning-with-Python
- **Description**: Small scale machine learning projects to understand the core concepts
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-07-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Machine-Learning-with-Python [![star this repo](http://githubbadges.com/star.svg?user=devAmoghS&repo=Machine-Learning-with-Python)](http://github.com/ddavison/github-badges) [![fork this repo](http://githubbadges.com/fork.svg?user=devAmoghS&repo=Machine-Learning-with-Python)](http://github.com/ddavison/github-badges/fork)
![alt text](https://media.istockphoto.com/vectors/machine-learning-3-step-infographic-artificial-intelligence-machine-vector-id962219860?k=6&m=962219860&s=612x612&w=0&h=yricYyUqZbILMHp3IvtenS3xbRDhu1w1u5kk2az5tbo=)

## Small scale machine learning projects to understand the core concepts
* Topic Modelling using **Latent Dirichlet Allocation** with newsgroups20 dataset, implemented with Python and Scikit-Learn
* Implemented a simple **neural network** built with Keras on MNIST dataset
* Stock Price Forecasting on Google using **Linear Regression**
* Implemented a simple a **social network** to learn basics of Python
* Implemented **Naives Bayes Classifier** to filter spam messages on SpamAssasin Public Corpus
* **Churn Prediction Model** for banking dataset using Keras and Scikit-Learn
* Implemented **Random Forest** from scratch and built a classifier on Sonar dataset from UCI repository
* Simple Linear Regression in Python on sample dataset
* **Multiple Regression** in Python on sample dataset
* **PCA and scaling** sample stock data in Python [working_with_data]
* **Decision Trees** in Python on sample dataset
* **Logistic Regression** in Python on sample dataset
* Built a neural network in Python to defeat a captcha system
* Helper methods include commom operations used in **Statistics, Probability, Linear Algebra and Data Analysis**
* **K-means clustering** with example data; **clustering colors** with k-means; **Bottom-up Hierarchical Clustering**
* Generating Word Clouds
* Sentence generation using n-grams
* Sentence generation using **Grammars and Automata Theory; Gibbs Sampling** 
* Topic Modelling using Latent Dirichlet Analysis (LDA)

## Installation notes
MLwP is built using Python 3.5.  The easiest way to set up a compatible
environment is to use [Conda](https://conda.io/).  This will set up a virtual
environment with the exact version of Python used for development along with all the
dependencies needed to run MLwP.

1.  [Download and install Conda](https://conda.io/docs/download.html).
2.  Create a Conda environment with Python 3.

    ```
    conda create -n *your env name* python=3.5
    ```

3.  Now activate the Conda environment.

    ```
    source activate *your env name*
    ```

4.  Install the required dependencies.

    ```
    ./scripts/install_requirements.sh

## How good is the code ?
* It is well tested
* It passes style checks (PEP8 compliant)
* It can compile in its current state (and there are relatively no issues)

## How much support is available?
* FAQs (coming soon)
* Documentation (coming soon)

## Issues
Feel free to submit issues and enhancement requests.

## Contributing
Please refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the "fork-and-pull" Git workflow.

 1. **Fork** the repo on GitHub
 2. **Clone** the project to your own machine
 3. **Commit** changes to your own branch
 4. **Push** your work back up to your fork
 5. Submit a **Pull request** so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!