Machine Learning Course with Python:

Overview

A Machine Learning Course with Python

https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat https://badges.frapsoft.com/os/v2/open-source.png?v=103 https://img.shields.io/twitter/follow/machinemindset.svg?label=Follow&style=social

Table of Contents

Download Free Deep Learning Resource Guide

Slack Group

Introduction

The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python.

Motivation

Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built our tutorials using many different well-known Machine Learning frameworks such as Scikit-learn. In this project you will learn:

  • What is the definition of Machine Learning?
  • When it started and what is the trending evolution?
  • What are the Machine Learning categories and subcategories?
  • What are the mostly used Machine Learning algorithms and how to implement them?

Machine Learning

Title Document
An Introduction to Machine Learning Overview

Machine Learning Basics

_img/intro.png
Title Code Document
Linear Regression Python Tutorial
Overfitting / Underfitting Python Tutorial
Regularization Python Tutorial
Cross-Validation Python Tutorial

Supervised learning

_img/supervised.gif
Title Code Document
Decision Trees Python Tutorial
K-Nearest Neighbors Python Tutorial
Naive Bayes Python Tutorial
Logistic Regression Python Tutorial
Support Vector Machines Python Tutorial

Unsupervised learning

_img/unsupervised.gif
Title Code Document
Clustering Python Tutorial
Principal Components Analysis Python Tutorial

Deep Learning

_img/deeplearning.png
Title Code Document
Neural Networks Overview Python Tutorial
Convolutional Neural Networks Python Tutorial
Autoencoders Python Tutorial
Recurrent Neural Networks Python IPython

Pull Request Process

Please consider the following criterions in order to help us in a better way:

  1. The pull request is mainly expected to be a link suggestion.
  2. Please make sure your suggested resources are not obsolete or broken.
  3. Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.
  4. Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.
  5. You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.

Final Note

We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.

Developers

Creator: Machine Learning Mindset [Blog, GitHub, Twitter]

Supervisor: Amirsina Torfi [GitHub, Personal Website, Linkedin ]

Developers: Brendan Sherman*, James E Hopkins* [Linkedin], Zac Smith [Linkedin]

NOTE: This project has been developed as a capstone project offered by [CS 4624 Multimedia/ Hypertext course at Virginia Tech] and Supervised and supported by [Machine Learning Mindset].

*: equally contributed

Citation

If you found this course useful, please kindly consider citing it as below:

@software{amirsina_torfi_2019_3585763,
  author       = {Amirsina Torfi and
                  Brendan Sherman and
                  Jay Hopkins and
                  Eric Wynn and
                  hokie45 and
                  Frederik De Bleser and
                  李明岳 and
                  Samuel Husso and
                  Alain},
  title        = {{machinelearningmindset/machine-learning-course:
                   Machine Learning with Python}},
  month        = dec,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.3585763},
  url          = {https://doi.org/10.5281/zenodo.3585763}
}
Comments
  • OF and LR updates

    OF and LR updates

    Taking into account review notes. Having trouble setting up my python environment, so I have not been able to test the code yet. I hope to fix that today/tomorrow. Fixed the table in LR.

    opened by BroccoliHijinx 11
  • Multilayer Perceptron write-up

    Multilayer Perceptron write-up

    Submitting a PR now to allow for comments on what is done. There are placeholders for what is left to be done, and I should be able to do that tomorrow.

    Left to do;

    images and associated text

    More on backprop

    Defining and explaining actual MLPs (most right now is on NN basics)

    opened by BroccoliHijinx 3
  • Addressed comments brought up in peer review

    Addressed comments brought up in peer review

    I decided to remove the multiple linear regression section because it seems beyond the scope of this module. Those images, MLR.png and MLR_POBF.png, can safely be removed from our image folder. I left a mention to it for completeness. I also added captions for all figures and equations to explain what they are.

    opened by b-sherman 3
  • Logistic Regression Files, some overfitting changes

    Logistic Regression Files, some overfitting changes

    Within Logistic Regression, I have a table that I cannot get working. I want to keep messing around with it, but I'm not sure what is wrong. I am using the rst basic table, but I think the spacing is off somehow.

    opened by BroccoliHijinx 3
  • Naive bayes question

    Naive bayes question

    Hi @astorfi , Thanks for your great work ! I'm a beginner of ML. Tonight when I learn Naive Bayes Classification in your tutorial, I found the Equation 1 in the tutorial is different from that in Wiki. I wonder which one is correct or both of them are right?

    image


    image

    Look forward to your reply.

    opened by suedroplet 2
  • Chinese Translation

    Chinese Translation

    Hi @astorfi , Thanks for your great work ! My friends and I have learned a lot here. China has a platform called KESCI (https://www.kesci.com). They provide algorithm competition opportunities for developers, which is similar to Kaggle, and self - training online environment to enhance their algorithmic ability. I am going to translate the whole series to Chinese and applied for a column to publish them on KESCI, as a series. Hope to get your permission. thanks.

    opened by Vivian0210 2
  • Overfitting rst file

    Overfitting rst file

    I don't think including code with this module makes much sense, so I just included a write-up. I tried to keep it short and simple, since this is something to keep in mind in the entire course.

    opened by BroccoliHijinx 2
  • Naive bayes

    Naive bayes

    I just created a new branch for the updated naive bayes files since the old one is very far behind now. Included are the images, code, and module text.

    opened by b-sherman 1
  • Linear regression

    Linear regression

    I redid all the linear regression code with a completely new data set to assure originality and because the existing scikit-learn ones are confusing to me so they are bound to be confusing to a new reader. I also changed all the images to reflect the new code. I tried to simplify the code as much as possible and only used the bare minimum number of references to scikit-learn functions. I also revised the rst document to reflect these changes. All generated images now have a link to the code I used to create them as well because it seemed like a good idea.

    opened by b-sherman 1
  • Updated linear_regression.rst

    Updated linear_regression.rst

    +Added a Motivation section that talks about what the problem is +Changed raw URLs into hyperlinks on smaller words +Added a Code section that links to the module code and talks about what it does +Added a Conclusion section to close out the module

    opened by b-sherman 1
  • Reference fixes

    Reference fixes

    Changed the "References" indent level in several modules to be consistent. Changed header casing in some modules to be consistent. Requesting merge so that the site can be updated for screenshots to include in the final project report.

    opened by b-sherman 0
Releases(1.0)
Owner
Instill AI
A company offering AI-based solutions to real-world applications.
Instill AI
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 05, 2023
Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python Open access and Code This repository contains the open access version of the text and the code examples in

Bayesian Modeling and Computation in Python 339 Jan 02, 2023
Tribuo - A Java machine learning library

Tribuo - A Java prediction library (v4.1) Tribuo is a machine learning library in Java that provides multi-class classification, regression, clusterin

Oracle 1.1k Dec 28, 2022
A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

Oleksii Trekhleb 1.4k Jan 06, 2023
Random Forest Classification for Neural Subtypes

Random Forest classifier for neural subtypes extracted from extracellular recordings from human brain organoids.

Michael Zabolocki 1 Jan 31, 2022
Visualize classified time series data with interactive Sankey plots in Google Earth Engine

sankee Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine Contents Description Installation Using P

Aaron Zuspan 76 Dec 15, 2022
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Axel 1.4k Jan 06, 2023
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

A library for debugging/inspecting machine learning classifiers and explaining their predictions

154 Dec 17, 2022
Library for machine learning stacking generalization.

stacked_generalization Implemented machine learning *stacking technic[1]* as handy library in Python. Feature weighted linear stacking is also availab

114 Jul 19, 2022
XManager: A framework for managing machine learning experiments 🧑‍🔬

XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction

DeepMind 620 Dec 27, 2022
Microsoft 5.6k Jan 07, 2023
Summer: compartmental disease modelling in Python

Summer: compartmental disease modelling in Python Summer is a Python-based framework for the creation and execution of compartmental (or "state-based"

6 May 13, 2022
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021
Uplift modeling and causal inference with machine learning algorithms

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Uber Open Source 3.7k Jan 07, 2023
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla

6.2k Jan 01, 2023
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 2022
Production Grade Machine Learning Service

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

Abdullah Zaiter 10 Apr 04, 2022
Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Trading Tesla with Machine Learning and Sentiment Analysis An interactive program to train a Random Forest Classifier to predict Tesla daily prices us

Renato Votto 31 Nov 17, 2022
ThunderGBM: Fast GBDTs and Random Forests on GPUs

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Xtra Computing Group 648 Dec 16, 2022