Here you have some good references on Reinforcement Learning:
Sutton RS, Barto AG. Reinforcement Learning: An Introduction. Cambridge, Mass: A Bradford Book; 1998. 322 p.
The draft for the second edition is available for free: Reinforcement Learning: An Introduction
Russell/Norvig Chapter 21:
Russell SJ, Norvig P, Davis E. Artificial intelligence: a ...
Most of the "standard textbooks" (e.g., Goldberg, Mitchell, etc.) are pretty dated now. If you just want to have some confidence that you understand how the basic algorithms work, they're fine, but they tend to emphasize material that's doesn't necessarily match the more modern way of understanding and talking about things like theoretical issues.
I've used ...
First of all, data only comes in so many forms that it might make sense to stick to a more "concrete definition". Data Science is necessarily practical. But here are a few other books with a more theoretical grounding. Others will certainly know many more...
A probabilistic theory of pattern recognition by Devroye, Györfi, Lugosi
An Introduction to ...
The two books that come into my mind are:
Artificial Intelligence: A Modern Approach
The Deep Learning Book
They both start from the basics and escalate while moving on.
Also thanks for your recommendation, I'll take a look at it because I want to jump to finance at some point in my career:)
The standard textbooks that covers AI is "Artificial Intelligence: A Modern Approach" by Russel & Norvig. The book's website can be found here.
I also recommend "Artificial Intelligence: Foundations of Computational Agents" by Poole and Mackworth. The book can be read online.
I would suggest instead of trying to get many sources, get one source that goes through concepts first (fairly robustly), then seek out sources to refine or deepen your knowledge. One source comes from Stanford's NLP group, and is Introduction to Information Retrieval. The only thing I don't like about this books is that it tends to orient documents as ...
[not a book but...] Definitely check out https://github.com/sebastianruder/NLP-progress for a self-updating list of relevant state-of-the-art literature in the field of NLP and its subfields.
As per the side projects that you mentioned, you might want to check e.g.
Hastie et al is at the mathematical level you require - being written by statistics academics with strong mathematical pedigree (Hastie is currently a mathematics professor, for example) - and the complete text is available for free online via the authors' website. It is probably about the best general survey of machine learning for people with mathematical ...
What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine learning itself, as it is as a part of Artificial Intelligence.
Simply stated the goal of Machine learning is two-fold: inference and prediction.
Inference: the ...
You should be able to translate code written in one language -- even pseudo-code -- to another, so I see no reason to avoid books for R. If you want one specifically for python, there's Machine Learning in Action by Peter Harrington.
One of scikit-learn's core committers is a releasing a book in October: Introduction to Machine Learning with Python: A Guide ...
Take a look at this free class Intro to Artificial Intelligence from Udacity. One of the instructors is Norvig. The class is more suitable for beginners than Norvig's book.
Even though this class doesn't have programming exercise, it explains concepts so well.
Its follow up class Artificial Intelligence for Robotics has programming exercises and does a ...
I recommend these books:
The nature of code: This book is a good introduction to GAs in general, and he has his own youtube channel with explanations and examples. A good starting point.
Genetic Algorithms in Java Basics: More in depth but very well explained and easy to understand, focused on java programming.
You can also see my answer here to have an ...
I refer you to a previous question I asked on CrossValidated for a similar request and still stand by my answer to this question.
Per @Coffee's recommendation, I would recommend the text Machine
Learning: A Bayesian and Optimization Perspective by Sergios
Theodoridis along with Pattern Recognition by the same author.
These two texts combined are ...
I can recommend Genetic Algorithms in Search, Optimization, and Machine Learning by Goldberg. In particular, chapter 1 gives a great "introduction to genetic algorithms with examples." The code examples are unfortunately in Pascal but readable even if not familiar with the language. The book by Thomas Back is a little more advanced but also more complete (...
I am reading Hands-On Machine Learning with Scikit-Learn and TensorFlow, its a great book (1/2 way thru). However it uses Scikit as per the title and some of the mechanics inside Sckit are a black box (not explained in depth). The general concepts however are still quite well written.
The 2nd half of the book is about TensorFlow so again, perhaps not what ...
There might not be an entire book about unsupervised techniques, given the greater effectiveness of supervised learning techniques. However, there are small books and selected chapters from longer books that could be relevant:
"Unsupervised Machine Learning in Python" which includes Gaussian Mixture Models
"Python Machine Learning" which has a section on ...
These 2 are pretty popular:
https://de.udacity.com/course/deep-learning--ud730/ - Tensorflow-Course by Google
http://www.fast.ai/ - Deep Learning by a Kaggle hero
The first is easy to follow and nicely presented, the second takes quite some time.
I guess its common to start with
"An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani" and then, if you want a deeper picture, move to
"The Elements of Statistical Learning, by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie".
I also really like the video course based on the first book, ...
There are numerous topics that you've mentioned but I will suggest those which I've read and are helpful.
For hidden markov models and markov processes I suggest reading Pattern Classification by Richard O. Duda. You can also take a look at Pattern Recognition and Machine Learning by Christopher Bishop. For better understanding Markov processes and their ...
AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out:
Machine Learning: A Probabilistic Perspective.
It covers everything from classical statistical methods to graphical models and deep learning. If you are specifically interested in topics having more to do with AI than machine learning, I think ...
For the latest practices of deep learning architectures, I follow the Kaggle latest Competition's notebooks.
Say, for example, In the recent finished Cassava Leaf Deasese Computer Vision Competition, People are sharing the experimental notebooks on different State of the art Architectures Like Vision Transformer(Various versions pretrained - ...
There are (at least) two recent and comprehensive ML books:
Probabilistic Machine Learning: An Introduction by Kevin Murphy.
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Both authors provide draft/raw versions for free online. The latter one however, as title already indicates, does not provide the same mathematical ...
There's a good chance that your question will be closed I'm afraid, but here are a few thoughts:
would differentiate a professional data scientist from me
A professional data scientist is somebody who does data science for a living, so you definitely belong to the club, congrats!
Seriously, apparently, you have at least some symptoms of the impostor ...
I would recommend Elements of Statistical Learning, by Trevor Hastie and Rob Tibshirani. That link gets the book directly from their Stanford website.
In addition, there are also accompanying videos (they follow the structure of a similar book), which are really helpful!
They offer a simpler set of books and online courses, which you can find listed here.