Its not only google lol Check the permissions for all your apps :)

Learn how to sell.

Watch wolf of wall street for reference.

Watching it for the 18th time right now, I think the mistake I'm making is not selling enough of nothing for something!

No.

linear algebra is a part of AI but AI is not just linear algebra. I would say linear algebra is used most with machine learning with is also a part of AI.

This diagram is what you're looking for. linear algebra would be its own bubble within AI. -https://qph.fs.quoracdn.net/main-qimg-9854e5bb9573b26c66caff...

Great diagram! Thank you

I had to clean my kitchen up.

You'll be good until we figure out how to make machines write enterprise ready software programs and the ability to maintain it on its owm. I think you have 10-20 years so you should be in management by then though so you have nothing to worry about :)

Safari books online. Best online learning resource ive ever used

There is tons of online tutorials for these things. Kaggle has good tutorials with the test datasets

Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path.

Here are the resources I found useful:

========================================== Advices from Open AI, Facebook AI leaders

Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)

Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)

Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-model...)

Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)

Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

From Open AI CEO Greg Brockman on Quora

Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg

2. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...)

Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks

Neural Nets — Andrej Karpathy’s CS231N (Stanford) http://cs231n.stanford.edu/

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/

Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora

In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.

What does physics have to do with ML/AI?

"The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe" https://www.technologyreview.com/s/602344/the-extraordinary-...

Thank you!