I'll take a practical, top-down approach, starting with high-level frameworks. I'll work on increasingly difficult problems, beginning with test problems with clean datasets and the move towards real-world problems. When I come across harder problems I'll consult ML friends. While solving problems, I'll try the most recommended MOOCs to fill knowledge gaps. I'll create a study group at School 42 to stay motivated.
I'll start by learning the basic Python syntax with Codeacademy and Google's Hello World and youtube series to learn Scikit learn. I'll start with the beginner problems on Kaggle, then do real world problems on their platform and then do a few request for research projects on OpenAI. While doing real problems, I'll try Udacity's intro to Machine Learning, Fast AI's course, Colombia's course, and Stanford's course.
This approach is inspired by this medium article and this quora post.
- http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
- https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850
- https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
- https://machinelearnings.co/a-humans-guide-to-machine-learning-e179f43b67a0
- http://a16z.com/2016/06/10/ai-deep-learning-machines/
- http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
- https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&index=1
- https://www.quora.com/What-are-the-best-ways-to-pick-up-Deep-Learning-skills-as-an-engineer
- https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c
- https://medium.com/@thisismetis/roadmap-how-to-learn-machine-learning-in-6-months-7c501889b545
- https://news.ycombinator.com/item?id=9902524
- https://metalmanac.com/topics/machinelearning/
- https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/
- http://www.erogol.com/machine-learning-pathway/
- https://www.edx.org/course/machine-learning-columbiax-csmm-102x-0
- https://www.coursera.org/learn/machine-learning
- http://course.fast.ai/
- https://www.udacity.com/course/intro-to-machine-learning--ud120
- https://www.coursera.org/learn/probabilistic-graphical-models
- https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
- https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
- https://www.udacity.com/course/cs373 AI for Robotics
- https://www.udemy.com/machinelearning/
- https://www.udacity.com/course/intro-to-statistics--st101
- https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
- https://www.coursera.org/course/neuralnets Neural Networks for Machine Learning
- https://www.coursera.org/course/nlangp Natural Language Processing
- https://www.coursera.org/course/aiplan AI Planning
- https://www.coursera.org/course/compneuro Computational Neuroscience
- https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/
- https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
- http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/
- https://www.youtube.com/playlist?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3
- https://www.udacity.com/course/intro-to-machine-learning--ud120
- https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
- https://www.kaggle.com/c/titanic
- https://www.kaggle.com/c/digit-recognizer
- https://www.kaggle.com/c/house-prices-advanced-regression-techniques
- https://www.kaggle.com/c/word2vec-nlp-tutorial
- https://openai.com/requests-for-research/#cartpole
- https://openai.com/requests-for-research/#funnybot
- https://openai.com/requests-for-research/#im2latex
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent