nachoag76/machine-learning-with-python-1

Small scale machine learning projects to understand the core concepts

Machine-Learning-with-Python

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. 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.

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!