GitHub - avinkon/great_courses_ml: code and data for ML-teachco-9070

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Name		Name	Last commit message	Last commit date
Latest commit History 224 Commits
data		data
imgs		imgs
Introduction to Machine Learning L02.pdf		Introduction to Machine Learning L02.pdf
L01aux.ipynb		L01aux.ipynb
L01qs.ipynb		L01qs.ipynb
L02.ipynb		L02.ipynb
L02qs.ipynb		L02qs.ipynb
L03.ipynb		L03.ipynb
L03aux.ipynb		L03aux.ipynb
L03qs.ipynb		L03qs.ipynb
L04.ipynb		L04.ipynb
L04qs.ipynb		L04qs.ipynb
L05.ipynb		L05.ipynb
L05qs.ipynb		L05qs.ipynb
L06.ipynb		L06.ipynb
L06qs.ipynb		L06qs.ipynb
L07.ipynb		L07.ipynb
L07aux.ipynb		L07aux.ipynb
L07qs.ipynb		L07qs.ipynb
L08.ipynb		L08.ipynb
L08aux.ipynb		L08aux.ipynb
L08qs.ipynb		L08qs.ipynb
L09.ipynb		L09.ipynb
L09aux.ipynb		L09aux.ipynb
L09qs.ipynb		L09qs.ipynb
L10.ipynb		L10.ipynb
L10qs.ipynb		L10qs.ipynb
L11.ipynb		L11.ipynb
L11qs.ipynb		L11qs.ipynb
L12.ipynb		L12.ipynb
L12qs.ipynb		L12qs.ipynb
L13.ipynb		L13.ipynb
L13qs.ipynb		L13qs.ipynb
L14.ipynb		L14.ipynb
L14aux.ipynb		L14aux.ipynb
L14qs.ipynb		L14qs.ipynb
L15.ipynb		L15.ipynb
L15qs.ipynb		L15qs.ipynb
L16.ipynb		L16.ipynb
L16qs.ipynb		L16qs.ipynb
L17.ipynb		L17.ipynb
L17qs.ipynb		L17qs.ipynb
L18.ipynb		L18.ipynb
L18aux.ipynb		L18aux.ipynb
L18qs.ipynb		L18qs.ipynb
L19.ipynb		L19.ipynb
L19aux.ipynb		L19aux.ipynb
L19qs.ipynb		L19qs.ipynb
L20.ipynb		L20.ipynb
L20qs.ipynb		L20qs.ipynb
L21.ipynb		L21.ipynb
L21qs.ipynb		L21qs.ipynb
L22.ipynb		L22.ipynb
L22qs.ipynb		L22qs.ipynb
L23.ipynb		L23.ipynb
L23qs.ipynb		L23qs.ipynb
L24.ipynb		L24.ipynb
L24qs.ipynb		L24qs.ipynb
L25.ipynb		L25.ipynb
L25qs.ipynb		L25qs.ipynb
README		README
requirements.txt		requirements.txt

Repository files navigation

************************************************************************
************************************************************************

This README describes the code and data provided to accompany
"Introduction to Machine Learning" (ML-teachco-9070), taught by Michael
Littman for The Great Courses.

For more information getting started, please see the accompanying file:

- Introduction to Machine Learning L02.pdf: This pdf is the section
from the course guidebook that details how to get started with Python
notebooks and Colab. For access to the rest of the guidebook, sign up
for the course itself at TheGreatCourses.com!

The program files provided here consist of three types:

- FOLLOW ALONG during or after lessons using the program files with
simple names like L01.ipynb, L02.ipynb, and so on. They are in the
.ipynb format discussed in Lesson 02 and are included for each of the
specific lessons, from 02 to 25.

- AUXILIARY ("aux") files are provided for background information for
those curious to explore other elements that appear on screen during
lessons. Files with names like L01aux.ipynb, L03aux.ipynb, and so on
are used by the instructor in the lesson, but that are not discussed
explicitly. Not every lesson has one of these "auxillary" files. Note:
Even users making diligent use of the FOLLOW ALONG and QUESTIONS
program files can skip the AUXILIARY files.

- QUESTIONS ("qs") FOR MORE PRACTICE are program files with names like
L01qs.ipynb, L02qs.ipynb, and so on. They are associated with the
practice problems for each lesson (which appear in the guidebook
for each lesson as question number 3).

Direct links to all program files are provided through the course
guidebook and through The Great Courses website. You can also access
them directly using a link like:

https://colab.research.google.com/github/mlittmancs/great_courses_ml/blob/master/L02.ipynb .

This particular link is the program associated with the "Starting with
Python Notebooks and Colab" lesson, Lesson 02. To access any other file,
simply take out the L02 in the above link and put in L16qs, or whatever
the name of the file is that you want to work with next.
************************************************************************

This file repository also includes three other sets of files necessary
to run the program files:

- imgs: A directory of images used by the program files.

- data: A directory of local datasets used by the program files.

- requirements.txt: A file required by colab that lists library
dependencies.

- README: This file.

************************************************************************

Frequently asked questions: Errata, clarifications, and other tips we
discover will be listed below.