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https://github.com/adeshpande3/Machine-Learning-Links-And-Lessons-Learned#learning-machine-learning
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https://towardsdatascience.com/get-your-computer-ready-for-machine-learning-how-what-and-why-you-should-use-anaconda-miniconda-d213444f36d6 (Introduction to Anaconda software)
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https://www.youtube.com/watch?v=HBxCHonP6Ro&list=PL6gx4Cwl9DGAcbMi1sH6oAMk4JHw91mC_
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https://github.com/dsciitpatna/Python/tree/master/LearningPython (Contains google colab link for hands-on experience in python)
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https://www.coursera.org/learn/machine-learning (Andrew NG's machine learning course, which is highly recommended for beginners)
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https://medium.com/analytics-vidhya/python-implementation-of-andrew-ngs-machine-learning-course-part-1-6b8dd1c73d80 (Python implementation of Andrew NG's course)
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https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi (3Blue1Brown Playlist on neural networks)
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https://www.udacity.com/course/deep-learning-pytorch--ud188 (Pytorch Udacity free course.)
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http://www.deeplearningbook.org/ (Online version of the Deep learning book by Ian Goodfellow, Benigo, Courville)
- http://cs231n.stanford.edu/ (CS231n : Stanford university's CNN for visual recognition)
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https://www.coursera.org/learn/language-processing (An excellent course on NLP covering topics from basics of NLP to intermediate level)
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http://web.stanford.edu/class/cs224n/ (CS224n : Stanford University's NLP with deep learning)
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https://github.com/Nish-19/Learn-Natural-Language-Processing-Curriculum (The path to be followed to learn NLP. Contains a list of topics along with the corresponding resources)