ml-training-intro

by amueller

Materials for the "Introduction to Machine Learning" class

214 Stars 139 Forks Last release: Not found MIT License 81 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

Introduction to Machine learning (with Andreas Mueller)

Instructor


This repository will contain the teaching material and other info associated with the "Introduction to Machine Learning" course.

Content

Obtaining the Tutorial Material

If you are familiar with git, it is probably most convenient if you clone the GitHub repository. This is highly encouraged as it allows you to easily synchronize any changes to the material.

git clone https://github.com/amueller/ml-training-intro.git

If you are not familiar with git, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/amueller/ml-training-intro) in your browser and click the green “Download” button in the upper right.

Please note that I may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and forked/cloned the repository via GitHub, you can sync your existing fork with via the following commands:

git pull origin master

Installation Notes

This tutorial will require recent installations of

The last one is important, you should be able to type:

jupyter notebook

in your terminal window and see the notebook panel load in your web browser. Try opening and running a notebook from the material to see check that it works.

For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a Python distribution such as Anaconda, which includes the most relevant Python packages for science, math, engineering, and data analysis; Anaconda can be downloaded and installed for free including commercial use and redistribution. The code examples in this tutorial should be compatible to Python 2.7, Python 3.4 and later. However, it's recommended to use a recent Python version (like 3.5 or 3.6).

After obtaining the material, we strongly recommend you to open and execute a Jupyter Notebook

jupter notebook check_env.ipynb
that is located at the top level of this repository. Inside the repository, you can open the notebook by executing
jupyter notebook check_env.ipynb

inside this repository. Inside the Notebook, you can run the code cell by clicking on the "Run Cells" button as illustrated in the figure below:

Finally, if your environment satisfies the requirements for the tutorials, the executed code cell will produce an output message as shown below:

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.