Machine learning and deep learning are not trivial subjects. The breadth of the material is pretty amazing. How in the world can we wrap our heads around this mass of information? I would like to share my top 6 online video series about ML and DL. All are available on YouTube for free. These are not in any particular order. I hope you find them helpful - I definitely did!
Beginners - the places to get acquainted
1. Deep Lizard
Deep Lizard's (@deeplizard) "Machine Learning & Deep Learning Fundamentals" course includes 36 videos, all of which are under 15 minutes, with most being much shorter. They give a very good overview with each topic broken down into bite-sized pieces. Great place to start.
Grant Sanderson's (@3blue1brown) series of videos on "Neural Networks" nicely combines high level overviews, somewhat deeper explanations, and some fun math in his videos. Plus, he uses the word "squishify" in his video and that makes me laugh.
3. Khan Academy
How can you not like Khan Academy (@khanacademy)? They have classes on pretty much everything you could ever want to learn, taught by great teachers, in easy to understand language. Their "Machine Learning" course has 160 videos (don't feel like you have to watch them all). Just pick the videos for the specific section you are interested in and enjoy!
Intermediate - go a little deeper and do some math
4. Andrew Ng - Coursera
Andrew Ng (@AndrewYNg) is one of the godfathers of AI and Machine Learning. His "Machine Learning" series from Coursera is probably the best intermediate machine learning class I have found. There is plenty of math and background, but it is a nice continuation of what you learned in the DeepLizard, 3blue1brown, and Khan Academy videos. Did I mention that he is former VP & Chief Scientist of Baidu; Co-Chairman and Co-Founder of Coursera; an Adjunct Professor at Stanford University; Founder and CEO of Landing AI; and Founder of deeplearning.ai? Oh, and he also founded and lead the Google "Brain Project." Wow! When he speaks, we should listen.
Advanced - pretty much as deep as you can go short of writing your dissertation
5. CS229 - Machine Learning - Stanford University
This is the Stanford University full semester class taught by Andrew Ng and some grad students in Autumn 2018. The class is 20 videos, each around 1 hour and 20 minutes. This is about as deep as you can go into the topic. Well worth the effort. If you are really feeling motivated, check out the syllabus which has the assignments. They are NOT easy! http://cs229.stanford.edu/syllabus-autumn2018.html
6. CS231n - Convolutional Neural Networks for Visual Recognition - Stanford University
This is the Stanford University full semester class taught by Fei-Fei Li (@drfeifei), Serena Yeung (@syeung10), and Justin Johnson (@jcjohnss). Fei-Fei Li is world renown for her work in computer vision. Serena and Justin are both masterful in their teaching. The class is 16 videos, each around 1 hour and 20 minutes. This is about as deep as you can go into the topic. Well worth the effort. If you are really feeling motivated, check out the syllabus which has the assignments. They are NOT easy! http://cs231n.stanford.edu/2017/syllabus
If you have questions and want to connect, you can message me on LinkedIn or Twitter. Also, follow me on Twitter @pacejohn and LinkedIn https://www.linkedin.com/in/john-pace-phd-20b87070/.
#artificialintelligence #ai #machinelearning #ml #convolutionalneuralnetworks #cnn #computervision #neuralnetworks #learning #stanford #coursera #khanacademy #3blue1brown #deeplizard