You are about to start working on the first Python assignment for our course. Before doing so, please carefully read the instructions below . It will help you to avoid various setup problems.

• Every week you will have an IPython notebook with instructions and some code prepared for you ( written in Python 3! ). You will need to work through it and complete. All assignments and related materials can be found in the GitHub repository of our course.

• We expect that you run the code locally . Clone or download our GitHub repository and don't forget to update the files, e.g. when you start each new assignment in case of bug fixes.

• Conveniently, you will be able to download all data for the tasks right from the notebooks with our download utils. Just make sure, that you have the folder 'common' from our repository in place in your local version.

• For peer-review assignments you will submit your notebooks manually to Coursera platform. For the programming assignments the submission of the graded parts is happening right from the notebooks .

• If you have some troubles with downloading the data from notebooks, you could download it manually here . There is also an old version of the assignment located there — please ignore it.

You will work with many libraries, e.g. NLTK, Scikit-learn, Gensim, Tensorflow, etc. You have several options on how to set it up:

1. (Recommended) Use the Docker container of our course. It already has all libraries, that you will need. The setup for you is very simple: install Docker application depending on your OS, download our container image, run everything within the container. Please, see a detailed Docker tutorial on our github.

2. Manually install all the libraries depending on your OS (each task contains a list of needed libraries in the very beginning). If you use Windows/MacOS you might find useful Anaconda distribution which allows to install easily most of the needed libraries. However, some tools, like StarSpace for week 2, are not compatible with Windows, so it's likely that you will have to use Docker anyways.

It might take a significant amount of time and resources to run the assignments code, but we expect that an average laptop is enough to accomplish the tasks. All assignments were tested in the Docker on Mac with 8GB RAM. If you have memory errors, that could be caused by not tested configurations or inefficient code. Consider reporting these cases or double-checking your code.

Alright... would do I do right now?

1. Go to our GitHub repository . If you are not familiar with git and GitHub, the easiest way for you might be to download the repo as a zip-archive with the green 'Clone or download' button. Otherwise, feel free to clone the repo with git clone command . For this, you will need git installed .

2. Figure out how to work in the Docker container . Alternatively, install Python 3 (and required libraries later on).

3. Open the notebook for the first week and start coding! After you are done, submit the results just by running some special cells in the same notebook (you will find all instructions inside).

As a final project, you will build a Telegram bot and submit it for peer review. To keep the bot up and running while the review process, we suggest that you host it on AWS free tier machine . The detailed AWS tutorial can help you to set it up. You might also consider AWS or other cloud virtual machines for fulfilling programming assignments, in case you have troubles with a local setup (however, keep in mind that free tier AWS machines have only 1 GB RAM, which is enough for the running bot, but not enough for other assignments in the course).

With all this said, best of luck with the practice part of our course!