[TBD]Learning guide for Machine learning

Resources

https://github.com/nltk/nltk/wiki/FAQ -> NLTK is a leading platform for building Python programs to work with human language data

https://www.researchgate.net/profile/Bob-Duncan/publication/324521739_CLOUD_COMPUTING_2018_Proceedings_of_the_Ninth_International_Conference_on_Cloud_Computing_GRIDs_and_Virtualization/links/5ad24f1d0f7e9b2859343862/CLOUD-COMPUTING-2018-Proceedings-of-the-Ninth-International-Conference-on-Cloud-Computing-GRIDs-and-Virtualization.pdf#page=121 -> A Comparison and Critique of Natural Language Understanding Tools

https://scikit-learn.org/stable/ -> Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning.

https://aws.amazon.com/sagemaker/ -> Cloud based machine learning from AWS Amazon

https://azure.microsoft.com/en-in/services/machine-learning/#product-overview -> Azure based machine learning from Microsoft

https://www.ibm.com/in-en/cloud/watson-studio -> IBM cloud based machine learning

https://www.tensorflow.org/ -> It is an open source artificial intelligence library, using data flow graphs to build models . It provides the core open source library to help you develop and train ML models.

https://keras.io/ -> Keras allows users to productize deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine.

https://caffe.berkeleyvision.org/ -> Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.