"Python Machine Learning" is a pretty good book. I also like "Natural Language Annotation" which is a bit specialized but there aren't all that many books on the annotation process.

Interesting, my only 2 encounters with Packt have been positive. One was a talk from a guy who wrote a book about a Python library who said Packt were good to work with, and the other was one of those pearls, "Python Machine Learning" (https://www.packtpub.com/big-data-and-business-intelligence/...)

Thanks for putting this together. I recommend you check out

https://sebastianraschka.com/

the author of Python Machine Learning. He's great at explaining things, wrote the bets intro to ML book IMHO and is a good Twitter follow.

I also humbly submit my own guide, from the perspective of how to approach studying it amidst all the resources available:

http://karlrosaen.com/ml/

Python Machine Learning is a great book to start the author made a great curriculum if you would like to follow it. https://sebastianraschka.com/faq/docs/ml-curriculum.html

This is mostly ML if you would like to dive into deep learning I think fast.ai is the best course for anyone with programming experience and you can also use the deeplearning.net Tutorial as a side reference.
If you have a practical experience and would love to understand the theory behinds it then Deep Learning Book is the Bible.

The problem is most of the people here don't understand how tough a problem is.
Once you figure that out then search for books for answer.
Here is a good book to start with Python Machine Learning.

Also don't read any book because it is free, Barber book is heavy in maths, you need at least a college level calculus and advanced statistics/ probability course to understand it.

Some on this thread have recommended Norvig's PAIP, but that's kind of an old school AI book in that it focuses on heuristic search and logic (implementing prolog in lisp at one point, very impressive stuff actually); but is lacking any coverage of statistical machine learning, which is the approach that underlies most of the cool stuff these days. It's still a great book, but I'd instead recommend a path that focuses on machine learning:

- The Master Algorithm: made for a general audience, gives you a lay of the land

- Python Machine Learning by Sebastian Raschka: gives you practical skills using python, scikit-learn, numpy, jupyter notebooks, pandas etc. From zero to kaggle in 4 chapters, goes deeper after that. Also goes into enough theory you aren't flying completely blind.

After that, I'm afraid I think you do need to go "academic", if by that you mean learning some of the underlying math to approach AI / ML from a more rigorous probabilistic perspective. I'd recommend studying probability theory and then working your way through Bishop's Pattern Recognition and Machine Learning. After that a lot more doors open up too more specialized topics like computer vision, reinforcement learning etc.

I've written up a lot more about this here:

http://karlrosaen.com/ml/

Sebastian is also the author of “Python Machine Learning” https://www.amazon.com/dp/1789955750/

I just got this book ~2 weeks ago and am using it in conjunction with udacity's course. Python Machine Learning (PML) is more low-level, math intensive, and hands on than udacity and other resources I'm using.

Thus far Udacity's course fits your explanation of using sklearn docs, tweaking inputs, etc. while PML provides a technical explanation of weighting with perceptrons and then you hand-write a perceptron learning algorithm.

Francois Cholet (author of Keras)'s Deep Learning with Python will be complete and fully published on the 20th of December: https://www.manning.com/books/deep-learning-with-python The chapters released so far are very good, the outlook chapters were extensively discussed on HN: https://blog.keras.io/the-limitations-of-deep-learning.html and https://blog.keras.io/the-future-of-deep-learning.html

Wickham's R for Data Science came out in December 2016, I'd like to pretend that counts as 2017: http://r4ds.had.co.nz/
It's a very complete introduction to the tidyverse which makes working in R much more pleasurable.

The second edition of Python for Data Science came out in October 2017 - that one focuses on Pandas, numpy and Jupyter notebooks, reasonably good introduction to those libraries.

The second edition of Sebastian Raschka's Python Machine Learning came out 2017 too - that one focuses more on scikit-learn and tensorflow, have only heard good things but haven't read much in it.

More great Jupyter Notebooks in the AI field:

1) 16 notebooks from the book "Python Machine Learning" by Raschka & Mirjalili
https://github.com/rasbt/python-machine-learning-book-2nd-ed...

2) Linear Regression, Logistic Regression, Random Forests, and k-Means Clustering notebooks by Nitin Borwankar
https://github.com/nborwankar/LearnDataScience

3) scikit-learn tutorial notebooks by Jake VanderPlas
https://github.com/jakevdp/sklearn_tutorial

4) Lots of deep learning notebooks from the book "Deep Learning with Python" by François Chollet
https://github.com/fchollet/deep-learning-with-python-notebo...

Bonus) Jupyter notebook on AWS tutorial (when your local computer just won't handle your notebook requirements):
http://efavdb.com/deep-learning-with-jupyter-on-aws/

Please share your jupyter notebook recommendations.

I like doing data science courses for fun. These days I am reading CS 229 mathematics and a book Python Machine Learning. Once in a while I get tired of coding in JS, or adding things to side projects; and then I like doing simple learning, just for the sake of learning and trying to understand (no strings attached). I like the maths part of data-science and maybe would pursue it as a theoretical endeavor. Or just repeat what people have posted on GitHub and see the results - like kids soldiering circuits. It would be fun to take part in some Kaggle competitions next year, around healthcare. Back in 2014, I spent some time working on an SVM classifier for news.

Going through:

* Faust - Johann Wolfgang von Goethe

* How To Read a Book - Mortimer J. Adler

* Python Machine Learning 2nd Edition - Sebastian Raschka

* Economics 19e - Samuelson/Nordhaus

Next in line:

* The Sleepwalkers - Hermann Broch

* The Will to Power - Friedrich Nietzsche

* Oxford Handbook of Business and Government - David Coen(and other dozens of lecturers)