Data science from scratch
To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability--and how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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- The handouts at https://fleuret.org/dlc/ are fantastic.
Also two books: Data Science from Scratch and Deep Learning from Scratch. They are more hand-on, but you'll build all the low-level things in Python and learn a lot.
- For your specific problem (avoiding the use of libraries that do too much of the work), two sources I found useful:
- Data Science from Scratch, by Joel Grus
- The first 5 deeplearning.ai courses (whose exercises relied only on numpy IIRC)
- I think all serious researchers have implemented core Deep Learning algorithms from scratch. I know I have.
There are two books that do exactly this:
1. Deep Learning from Scratch
2. Data Science from Scratch
In these books, you implement each part of the ML/DL pipeline, from scratch, in Python.
There is also a GitHub project call Minitorch that teaches you the inner workings of a framework like PyTorch.
And then there are several other good resources for exactly this.
What he claims to have as a content is neithet new nor unique.
- 1. Nature of Code- simulating and modeling many things in nature.
2. Data Science from Scratch- building a Data Science library from scratch.
3. Deep Learning from Scratch- building a Deep Learning library from scratch.
4. Realm of Racket and 5. Land of Lisp- developing games with Racket and Lisp.
6. The Little Schemer- not only teaches you to think recursively, but also how to implement basic functions like addition, multiplication, list-related function from using Scheme for Scheme.
7. Genetic Algorithms in Elixir
8. Genetic Algorithms and Machine Learning for Programmers
- This material is focused on teaching how to develop a Deep Learning library, and not teaching Deep Learning through implementing things from scratch.
If that's what you want, then there are:
- Data Science from Scratch
- Deep Learning from Scratch
- I think Andrew Ng's course on Coursera is targeted towards a different audience. You are pretty good with math and I think you'll appreciate his CS229 Stanford course more, as he goes through the math, and does right off the bat on the blackboard/whiteboard. He goes covers gradient descent in the first lecture [after the intro lecture]. 2008[0] and 2018[1] versions.
Yaser Abu-Mostafa has CS156 at Caltech, a course and a textbook called "Learning from Data"[2][3].
It depends where you want to go. If you want to learn Python libraries like Tensorflow and Pytorch, Keras, and the like... there are many books for that.
"Deep Learning with Python", by François Chollet.[4][5]
"Data Science from Scratch: First Principles with Python", by Joel Grus.[6][7]
There are the fast.ai courses on https://fast.ai and also have a book titled:
"Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard and Sylvain Gugger. They have the lectures for several years on machine learning and deep learning.
There are many books and resources on how to train models. However, there are extremely few that enable you to do ML in a non toy project setting and do useful things in the real world.
One recent course is CS329s "Machine Learning Systems"[8] that fits nicely after CS229. I only skimmed some slides, but it appears to be interesting.
Disclaimer: Yes many publications about deployment, and ML project lifecycle management and platforms, but I have blocked most of them, including the largest publication about data science and machine learning on Medium from search results in my browser, because it publishes garbage posts from people who seem to never have worked on a project with actual stakes.
I know it was not your question, but I guess you'd like to do something with ML in the real world. There is a range of skills and process necessary to make things happen. Identifying the right problem, designing the right metrics (tricky, especially when working with a project involving "intangibles"). Reframing. These also require interviewing skills and deep dives into a subject matter (so the ability to discover and curate the right resources, then read and synthesize) to be able to interact with domain experts and see a project through.
- [0]: https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599
- [1]: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXSh...
- [2]: https://www.youtube.com/playlist?list=PLD63A284B7615313A
- [3]: https://work.caltech.edu/telecourse
- [4]: https://www.manning.com/books/deep-learning-with-python
- [5]: https://github.com/fchollet/deep-learning-with-python-notebo...
- [6]: https://www.oreilly.com/library/view/data-science-from/97814...
- [7]: https://github.com/joelgrus/data-science-from-scratch
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