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Machine Learning Algorithms

This repo is developed by Emanuel Fontelles.*

Theses notebooks contained here are machine learning algorithms and some Python libraries useful notebooks as Scikit-Learn

Scikit-Learn Algorithms:

** try to understand the algoritms first, then move to use sklearn module **

• Scikit-Learn Module Python:
  • Supervised learning
    • Classification and Linear Regression
    • K Nearest Neighbors (kNN)
    • Support vector machine (SVM)
  • Unsupervised learning
    • Clustering:
      • K-means
    • Dimensionality reduction PCA
    • Random Forests
    • Density GMM

Coursera - Machine Learning:

* Linear Regression
* Logistic Regression
* Neural Networks
* Backward Propagation
* Advice for Applying Machine Learning   

Udacity - Introduction to Machine Learning?:

Tensorflow and Tensorboard:

Usage and Installation Notes

Usage

You can view the tutorial materials using the excellent service from Binder. Click in the Binder bagde to play with the notebooks from your browser without installing anything or you can setup a local instalation.

Binder lets you easily host interactive Jupyter notebooks and let anyone on the internet use them interactively immediately! Binder creates executable environment making your code immediately reproducible by anyone, anywhere.

You can visualize the notebooks without running any kernel.

Local Instalation

This tutorial requires the following packages:

• Python version 2.7 or 3.6+
• numpy version 1.15 or later: http://www.numpy.org/
• scipy version 0.10 or later: http://www.scipy.org/
• matplotlib version 2.2.2 or later: http://matplotlib.org/
• scikit-learn version 0.19.2 or later: http://scikit-learn.org
• ipython version 2.0 or later, with notebook support: http://ipython.org
• seaborn version 0.8 or later: https://seaborn.pydata.org/

For a local installation, please follow the tutorial bellow. If you don't know how to install those on your platform, I recommend to install Miniconda, a distribution of the conda package and environment manager. Please follow the below instructions to install it and create an environment for the repository.

1. Download the Python 3.x installer for Windows, macOS, or Linux from https://conda.io/miniconda.html and install with default settings. Skip this step if you have conda already installed (from Miniconda or Anaconda). Linux users may prefer to use their package manager.
   • Windows: Double-click on the .exe file.
   • macOS: Run bash Miniconda3-latest-MacOSX-x86_64.sh in your terminal.
   • Linux: Run bash Miniconda3-latest-Linux-x86_64.sh in your terminal.
2. Open a terminal. Windows: open the Anaconda Prompt from the Start menu.

Once this is installed, the following command will install all required packages in your Python environment:

$ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn

Alternatively, you can download and install the (very large) Anaconda software distribution.

Every time you want to work, do the following:

1. Open a terminal. Windows: open the Anaconda Prompt from the Start menu.
2. Start Jupyter with jupyter notebook or jupyter lab. The command should open a new tab in your web browser.
3. Edit and run the notebooks from your browser.

Downloading the Tutorial Materials

I would highly recommend using git, not only for this tutorial, but for the general betterment of your life. Once git is installed, you can clone the material in this tutorial by using the git address shown above:

git clone git://github.com/emanuelfontelles/machinelearning.git

If you can't or don't want to install git, there is a link above to download the contents of this repository as a zip file. I may make minor changes to the repository in the days before the tutorial, however, so cloning the repository is a much better option.

External Repositories

This repository contain some notebooks from other authors. The details of these repos are in the links follows:

Name	Author	License
sklearn_tutorial	Jake Vanderplas	MIT
PythonDataScienceHandbook	Jake Vanderplas	MIT

Disclaimer

This is a personal repository that is not meant for public use at this time. It is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. No installation or technical support will be provided.