Shivam Mishra (me-smishra27)

me-smishra27

Geek Repo

Company:Wells Fargo

Location:India

Home Page:https://www.linkedin.com/in/shivam-mishra01/

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Shivam Mishra's repositories

Complete-Python-3-Bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

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handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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Andrew-Ng-Machine-Learning-Notes

The offical notes of Andrew Ng Machine Learning in Stanford University

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Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

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mlviz

Visualizations of machine learning models and algorithms

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100_Days_Code_Challenge

100 Days of Code Challenge in Python

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Data-Science-Interview-Resources

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

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cornell-cs5785-applied-ml

Teaching materials for the applied machine learning course at Cornell Tech

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Resources-1

A list of resources to get you started in data science.

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cs229-2018-autumn

All notes and materials for the CS229: Machine Learning course by Stanford University

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TensorFlow-Course

:satellite: Simple and ready-to-use tutorials for TensorFlow

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awesome-normalization-techniques

Papers for normalization techniques, released codes collections.

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PyTorchNLPBook

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info

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ml-yearning

Machine Learning Yearning (full pdf)

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TED-talks

A natural language processing project to reveal linguistic features that predict a persuasive TED Talk. I webscraped every TED Talk transcript and it's metadata through 2017 and then used decision trees, random forest regressors, and linear regression to find key predictors of persuasive ratings by viewers.

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