Best blogs and resources for learning Python:
https://github.com/negiaditya/python_beginer
https://www.datacamp.com/courses/intro-to-python-for-data-science
http://scipy.github.io/old-wiki/pages/Tentative_NumPy_Tutorial
http://pandas.pydata.org/pandas-docs/stable/10min.html
http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
https://www.datacamp.com/courses/pandas-foundations
https://www.guru99.com/python-tutorials.html
Best blogs and resources for learning R:
https://www.datacamp.com/courses/free-introduction-to-r
https://www.coursera.org/learn/r-programming
https://www.dataquest.io/course/r-fundamentals
https://www.rdocumentation.org
Github repos:
https://github.com/alexeygrigorev/data-science-interviews
https://github.com/aaronwangy/Data-Science-Cheatsheet
https://github.com/khanhnamle1994/cracking-the-data-science-interview
https://github.com/youssefHosni/Data-Science-Interview-Questions-Answers
https://github.com/alirezadir/Machine-Learning-Interviews
Online courses for Data Science
Stanford Artificial Intelligence Laboratory
http://ai.stanford.edu/
https://ai.stanford.edu/courses/
Spring 2019 Full Stack Deep Learning Bootcamp
https://fullstackdeeplearning.com/march2019
A Course in Machine Learning
http://ciml.info/
STATS 202: Data Mining and Analysis
http://web.stanford.edu/class/stats202/
CS109: Intro to Probability for Computer Scientists
https://web.stanford.edu/class/cs109/index.html
CS221: Artificial Intelligence: Principles and Techniques
https://stanford-cs221.github.io/autumn2019/
Machine Learning
http://cs229.stanford.edu/
Deep Learning
https://cs230.stanford.edu/
Convolutional Neural Networks for Visual Recognition
http://cs231n.stanford.edu/
Natural Language Processing with Deep Learning
http://web.stanford.edu/class/cs224n/
Practical Deep Learning for Coders
https://course.fast.ai/
RL Course by David Silver
https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
CS234: Reinforcement Learning
http://web.stanford.edu/class/cs234/index.html
Some useful blog posts:
https://colah.github.io/
https://ofir.io/How-to-Start-Learning-Deep-Learning/
https://yerevann.com/a-guide-to-deep-learning/
http://adventuresinmachinelearning.com/neural-networks-tutorial/
https://www.springboard.com/blog/probability-bayes-theorem-data-science/
https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
https://www.saama.com/recognitions/2019-pm360-innovations-award/
https://www.saama.com/2019/12/
https://medium.com/@pmin91/aspect-based-opinion-mining-nlp-with-python-a53eb4752800
https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/
https://github.com/lgalke/vec4ir
https://allenai.github.io/scispacy/
https://github.com/wonjininfo/CollaboNet
https://github.com/nutli/concept_normalisation
https://towardsdatascience.com/how-do-they-apply-bert-in-the-clinical-domain-49113a51be50
https://medium.com/@adriensieg/text-similarities-da019229c894
https://mccormickml.com/2020/03/10/question-answering-with-a-fine-tuned-BERT/
https://medium.com/data-science-group-iitr/logistic-regression-simplified-9b4efe801389
https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220
https://medium.com/@SeattleDataGuy/what-is-a-decision-tree-algorithm-4531749d2a17
https://medium.com/machine-learning-101/chapter-5-random-forest-classifier-56dc7425c3e1
https://medium.com/machine-learning-for-humans/neural-networks-deep-learning-cdad8aeae49b
https://towardsdatascience.com/will-you-become-a-zombie-if-a-99-accuracy-test-result-positive-3da371f5134
https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72
https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d
Stats:
https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw
Deep Learning
https://cs230.stanford.edu/
Convolutional Neural Networks for Visual Recognition
http://cs231n.stanford.edu/
Natural Language Processing with Deep Learning
http://web.stanford.edu/class/cs224n/
Practical Deep Learning for Coders
https://course.fast.ai/
RL Course by David Silver
https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
CS234: Reinforcement Learning
http://web.stanford.edu/class/cs234/index.html
Stanford CS229: Machine Learning
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
Applied Machine Learning
https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83
Machine Learning with Graphs (Stanford)
https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn
Probabilistic Machine Learning
https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd
Deep Learning: CS 182
https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A
Deep Unsupervised Learning
https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP
NYU Deep Learning SP21
https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI
CMU Neural Networks for NLP
https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV
Multilingual NLP
https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5
Advanced NLP
https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL
Deep Learning for Computer Vision
https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r
Deep Reinforcement Learning
https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
Full Stack Deep Learning
https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv
System Design:
https://www.youtube.com/@jordanhasnolife5163/playlists
https://www.youtube.com/@AsliEngineering
https://netflixtechblog.com/system-architectures-for-personalization-and-recommendation-e081aa94b5d8
https://www.evidentlyai.com/ml-system-design
https://dev.to/mukeshkuiry/machine-learning-and-system-design-3d2f
llamasearch 