Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/mramshaw/intro-to-ml

Intro to Machine Learning - Pattern Recognition for Fun and Profit
https://github.com/mramshaw/intro-to-ml

machine-learning matplotlib ml numpy pandas pip pip3 python scikit-learn scipy seaborn seaborn-plots sklearn statsmodels tensorflow weka

Last synced: about 15 hours ago
JSON representation

Intro to Machine Learning - Pattern Recognition for Fun and Profit

Awesome Lists containing this project

README 

        
# Intro to Machine Learning - Pattern Recognition for Fun & Profit



[![Known Vulnerabilities](http://snyk.io/test/github/mramshaw/Intro-to-ML/badge.svg?style=plastic&targetFile=requirements.txt)](http://snyk.io/test/github/mramshaw/Intro-to-ML?style=plastic&targetFile=requirements.txt)



The table of contents is as follows:



* [Overview](#Overview)

* [Prerequisites](#prerequisites)

* [scikit-learn](#scikit-learn)

* [Libraries](#libraries)

    * [Numpy](#numpy)

    * [Matplotlib / Seaborn](#matplotlib--seaborn)

    * [StatsModels](#statsmodels)

* [requirements.txt](#requirementstxt)

* [TODO](#todo)

* [Credits](#credits)

* [Alternatives](#alternatives)

* [Data Cleaning](#data-cleaning)

* [Quick Hits](#quick-hits)

* [End to End](#end-to-end)

* [Tools](#tools)



## Overview



This is a nice free introduction to Machine Learning with Python.



![xkcd](http://imgs.xkcd.com/comics/machine_learning.png)



Here is how the folks at

[nVidia](http://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/)

see the relationship between Artifical Intelligence, Machine Learning and Deep Learning:



![AI_versus_ML_versus_Deep_Learning](/images/Deep_Learning_Icons_R5_PNG.jpg.png)



Towards the beginning of my career, I was interested in AI and joined a society founded

by [Donald Michie](http://www.theguardian.com/science/2007/jul/10/uk.obituaries1) - who

was then at the University of Edinburgh. I wonder how much things have progressed since

then?



Machine Learning is hot right now, and of course the cloud providers have noticed.



Here is Google's Cloud offering:



        http://cloud.google.com/products/machine-learning/



For a more sombre view of things, the following article is worth reading:



        http://www.cio.com/article/3223191/artificial-intelligence/a-practical-guide-to-machine-learning-in-business.html



## Prerequisites



[Chris Manning](http://profiles.stanford.edu/chris-manning), Stanford, 3 Apr 2017:



> "Essentially, Python has just become the [lingua franca](http://en.wikipedia.org/wiki/Lingua_franca) of nearly all the

> deep learning toolkits, so that seems the thing to use."



        http://youtu.be/OQQ-W_63UgQ?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6&t=2102



For an explanation of why Python (as contrasted with other languages) is a good choice for

[Natural language processing](http://en.wikipedia.org/wiki/Natural_language_processing)

the following link is worth a look:



        http://www.nltk.org/book_1ed/ch00-extras.html



1. Python (Python 2 support has been dropped from a number of projects, so use Python 3)



2. `pip` or possibly `pip3` (if using Python 2 __and__ Python 3)



`pip` (or `pip3`) is the Package manager for Python, much as `npm` is the package manager

for the [Node.js](http://nodejs.org/) platform.



## scikit-learn



The course uses this library, which it refers to as `sklearn`.



The latest version may be found here:



        http://scikit-learn.org/stable/



To install this library in multi-user mode (not recommended) with `pip` (replace with `pip3` if using Python 3):



        pip install -U scikit-learn



To install this library in single-user mode (recommended) with `pip` (replace with `pip3` if using Python 3):



        pip install --user scikit-learn



## Libraries



It's not really possible to do much of anything in Python without additional libraries.



Essential libraries include:



* [NumPy](http://www.numpy.org/)

* [SciPy](http://www.scipy.org/index.html)



Useful optional libraries include:



* [matplotlib](http://matplotlib.org/)

* [nltk](http://www.nltk.org/)

* [pandas](http://pandas.pydata.org/)



Verify library presence and version with `pip` as with `scikit-learn`:



    pip list --format=freeze | grep numpy



[Replace `numpy` above as necessary.]



Or verify library presence and version with Python:



    python -c "import numpy as im; print(im.__version__)"



[Likewise replace `numpy` above as necessary.]



Or use `try_import.py` for multiple libraries as shown:



```bash

$ python try_import.py numpy scipy sklearn keras pytorch

"numpy" was imported

"scipy" was imported

"sklearn" was imported

Using TensorFlow backend.

"keras" was imported

"pytorch" could not be imported - try "pip install --user pytorch"

$

```



Install the library with `pip` (either multi-user or single-user) as with `scikit-learn` above.



#### Numpy



NumPy allows for a nice performance optimization called __single instruction, multiple data__, or

[SIMD](http://en.wikipedia.org/wiki/SIMD).



Basically, this allows for vector or matrix handling (compare 'vectors\ pt1.py' to 'vectors\ pt2.py').



#### Matplotlib / Seaborn



Matplotlib is great for plotting variables, but can be very low-level.



To make these graphs look a little better, check out my [No More Blue](http://github.com/mramshaw/No_More_Blue) repo.



Or - for a higher-level library - check out [Seaborn](http://seaborn.pydata.org).



[Seaborn will greatly simplify a number of difficult `matplotlib` graphing exercises.]



#### StatsModels



Although not used in this course, [StatsModels](http://www.statsmodels.org/stable/index.html) is also worth a look.



It provides classes and functions for the estimation of many different statistical models, as well as for conducting

statistical tests, and statistical data exploration.



Some __Seaborn__ functions will optionally use StatsModels if it is installed.



## requirements.txt



Of course, it's also possible (as with __npm__ or __composer__) to install all dependencies in one fell swoop (probably a _best practice_).



Simply list the dependencies in a file (for example `requirements` or `requirements.txt`) and install from it:



        pip install --user -r requirements.txt



[Note the `--user` option, which may be omitted for a Global install, also the `-r` option to specify an input file.]



## TODO



- [ ] Finish course

- [x] Update Quick Hit links to make them easier to navigate

- [ ] Update everything for the most recent (and secure) version of TensorFlow



## Credits



Based upon:



        http://www.udacity.com/course/intro-to-machine-learning--ud120



You can find an interview with co-author Katie Malone here:



        http://www.se-radio.net/2017/03/se-radio-episode-286-katie-malone-intro-to-machine-learning/



## Alternatives



The following look like interesting options too:



        http://web.stanford.edu/class/cs224n/



        http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning



## Data Cleaning



A ___lot___ (lets say three quarters) of a data scientist's time is spent massaging data.

Which is a pretty important (lets say _critically_ important) part of a data scientist's

job and not often discussed.



___Garbage in, garbage out.___



[Not to mention the (very expensive) computer time wasted.]



For a quick introduction to data cleaning with `numpy` and `pandas`, have a look at this

great tutorial:



    http://realpython.com/python-data-cleaning-numpy-pandas/



You can see my stab at it [here](http://github.com/mramshaw/Data-Cleaning).



For a more complicated example, check out my [ML with Missing Data](http://github.com/mramshaw/ML_with_Missing_Data) repo.



## Quick Hits



For an easy (and quick) introduction to the various Python tools and ML concepts:



        http://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal



This series is from mid-2016 so there is a small amount of 'code rot', plus it seems to use

Python 2 rather than Python 3, but even so it's a quick and fun way to get a brief overview

of ML and the tools & techniques involved.



* [Machine Learning Recipes #1 (Hello World)](./Hello_World/)

* [Machine Learning Recipes #2 (Visualizing a Decision Tree)](./Iris/)

* [Machine Learning Recipes #3 (What Makes a Good Feature?)](./Features/)

* [Machine Learning Recipes #4 (Let's Write a Pipeline)](./Pipeline/)

* [Machine Learning Recipes #5 (Writing Our First Classifier)](./Custom_Classifier/)

* [Machine Learning Recipes #6 (Train an Image Classifier with TensorFlow for Poets)](./Image_Classifier/)

* [Machine Learning Recipes #7 (Classifying Handwritten Digits with TF.Learn)](./Handwriting_Classifier/)

* [Machine Learning Recipes #8 (Let's Write a Decision Tree Classifier from Scratch)](./Decision_Tree/)

* [Machine Learning Recipes #9 (Intro to Feature Engineering with TensorFlow)](./Feature_Engineering/)

* [Machine Learning Recipes #10 (Getting Started with Weka)](./WEKA/)



## End to End



For a deeper dive into the Iris dataset, check out my [ML with SciPy](http://github.com/mramshaw/ML_with_SciPy) repo.



This project shows a full end-to-end workflow.



## Tools



There are a number of tools, such as Python, IPython, and Jupyter Notebooks.



One website that gets a lot of mentions is [Anaconda](http://www.anaconda.com/):



    http://www.anaconda.com/download/