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  • Contact and Book
  • The Training Log


​The Global Nomad
(JAN-MAR 23 = PHILADELPHIA)

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Summer reading and homework: Machine Learning

6/16/2019

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Last year, for our annual developer and product conference with our global dev teams in Pune India, I made a presentation about lifelong-learning-and-continuous-education pointers on Machine Learning, as I explained that the lines between traditional software development, data science and artificial intelligence are totally blurring today. ( See my three posts here)

A year later, I have updated this list a little (and cut a lot of stuff from it to focus on what’s efficient for product and dev team members), and I thought I would post it for safe keeping and sharing.

The basics: 
Machine learning at Stanford University
The super famous class by Andrew Ng is now online and for free via coursera (You’ll need to pay if you want the graduation certificate though). The basic topics are covered from linear algebra refresher to linear regression to an intro to neural networks.
Stanford
AI at Columbia University
Superb starter course and really cool labs and projects. Free but you’ll also have to pay if you want the certificate of completion. (You might need to take a free Python class before getting started with this one)

columbia
Google AI Education
It’s free and it’s really well baked: videos, courses, workshops, documents, and even a “neural network playground”Provides a very good way to learn about artificial intelligence and I really like that the content is organized by type of individual like student, business person, researcher, curious cat etc.
GOOGLE
Udemy: Artificial Intelligence A-Z
Learn the basics and build a virtual Tesla (you’ll also learn the basics of python in the process, which is also good to take). Very cool, also not free if you want the certificate of completion.

Udemy
Then, the less basics:
MIT’s Intro to Deep Learning 
It’s a really good MOOC by MIT, also gets you to rub shoulders with TensorFlow during equallyu super cool lab sessions.
MIT
Intro to Reinforcement Learning at University College of London
By David Palmer. Markov, dynamic programming and policy gradient methods, it’s all there!!! It’s a really good refresher for those who haven’t touched this since they were puppies, like me.

LONDON

Let me know what you think! DM me @philippemora
​My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. 
​

Head of Product and VP Engineering.
thinker, doer, designer, coder, leader
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