Intro level resources for learning about machine learning

intro_learning_resources.txt

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GENERAL READING

Here are some general data science sites that could be worth a look. They contain links to lots of other good sites and resources. It's worth having a browse and seeing what captures your interest.

- http://partiallyderivative.com/news/2015/01/09/episode-9-the-one-that-will-totally-change-your-life
- http://blog.yhat.com/posts/ML-resources-you-should-know.html (summary of lots of resources)
- http://www.kdnuggets.com
- https://tinyletter.com/datamachina
- http://www.svds.com/blog/

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BOOKS

Here are some easy-to-read non-fiction books that I've found useful:

"Weapons of Math Destruction" is a good read about the biases and ethical issues involved in modern machine learning methods.
https://www.amazon.co.uk/Weapons-Math-Destruction-Increases-Inequality/dp/0141985410/ref=sr_1_1?s=books&ie=UTF8&qid=1513252239&sr=1-1&keywords=weapons+of+math+destruction

"The Master Algorithm" is a very readable overview of the different areas of machine learning.
https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0141979240/ref=sr_1_1?s=books&ie=UTF8&qid=1513252451&sr=1-1&keywords=the+master+algorithm


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FASHION TECH SPECIFIC

I like reading these company tech blogs:
- http://multithreaded.stitchfix.com/blog/
- https://making.lyst.com/

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MACHINE LEARNING SPECIFIC

These are courses that look very useful:

1. Hands-on workshop on image classification with DIGITS
https://nvidia.qwiklab.com/focuses/5866
This is a straightforward and quick tutorial, using NVIDIA DIGITS and Jupyter Notebooks, which lets you play with creating and improving some simple image classification models.

2. Coursera Machine Learning
https://www.coursera.org/learn/machine-learning
This is an oldie but goodie, it covers the basics of machine learning really well. Highly recommended. I think you can still "audit" the course for free.

3. FastAI machine learning 
http://course.fast.ai/
I've heard great things about this course - it's a very practical introduction to deep learning using Python and Keras.

These are videos that are really nice and easy to follow:
- https://www.youtube.com/watch?v=bxe2T-V8XRs - Neural Networks Demystified (5 videos) by Stephen Welch is a very clear and easy-to-follow explanation of how neural networks work.

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PYTHON COURSES

These are useful courses for gaining experience in Python.

These are good interactive walks through the basics. Pandas and Numpy are very useful for data science work.
https://www.learnpython.org/
https://www.datacamp.com/courses/intro-to-python-for-data-science

This tutorial provides a walkthrough of using SQL databases, a very useful tool when working with any kind of data, with Python. 
https://www.datacamp.com/courses/intro-to-sql-for-data-science

This site has lots of fun coding challenges, ranging from easy to hard. If you get stuck, you can see how other people have solved the same problem, and you can learn a lot from seeing the different approaches out there.
https://www.codewars.com/

The best way to learn Python is to use it. It doesn't really matter what you use it on. You can do an awful lot with Python, so it's a case of finding a pet project that really excites you. Here are some examples that could be exciting to explore.
- Making a game: https://www.raywenderlich.com/24252/beginning-game-programming-for-teens-with-python
- Making a website: https://opentechschool.github.io/python-flask/
- Doing statistical analyses: http://www.scipy-lectures.org/packages/statistics/index.html
- Making deep learning models: http://course.fast.ai/
- Making a desktop application: http://sebsauvage.net/python/gui/
- Here's lots of other ways that Python has been successfully used: https://www.python.org/about/success/


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MATHS BASICS

It'd be good to pick up some of the basics of linear algebra, calculus and probability/statistics.

Linear Algebra:
- https://www.khanacademy.org/math/linear-algebra - Khan Academy videos tend to be easy to follow.
- https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/ - An amazing set of video lectures by Prof Gilbert Strang from MIT, the definitive learning resource.
- http://rlhick.people.wm.edu/stories/linear-algebra-python-basics.html - A very simple intro to linear algebra with Python and Numpy.

Statistics
- https://conference.scipy.org/scipy2013/tutorial_detail.php?id=109 - This tutorial (videos + jupyter notebooks) looks quite comprehensive and practical.
- https://www.khanacademy.org/math/statistics-probability - Another good Khan Academy resource.

Calculus
- https://www.khanacademy.org/math/calculus-home - A solid video resource from Khan Academy.

Feel free to let me know about any other useful resources that you find, or if any of these aren't working or aren't that great. Thanks!