|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Deep Learning Coursera||5,253||4 years ago||24||mit||Jupyter Notebook|
|Deep Learning Specialization by Andrew Ng on Coursera.|
|Deeplearning.ai Summary||4,881||5 months ago||13||mit||Python|
|This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.|
|Start Machine Learning||3,589||3 months ago||4||mit|
|A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2023 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!|
|Courses||3,422||4 years ago||92||HTML|
|Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1|
|Datasciencecoursera||1,838||2 years ago||25||HTML|
|Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.|
|Machine Learning Curriculum||1,064||14 days ago||mit|
|:computer: Learn to make machines learn so that you don't have to struggle to program them; The ultimate list|
|Freeml||994||2 years ago||2|
|A List of Data Science/Machine Learning Resources (Mostly Free)|
|Natural Language Processing Specialization||624||a month ago||6||Jupyter Notebook|
|This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera|
|Deeplearning.ai||617||6 years ago||2|
|deeplearning.ai , By Andrew Ng, All video link|
This is a collection of IPython notebooks that I created while following Coursera's Data Analysis course by Jeff Leek, assistant professor in the Biostatistics Department of the Johns Hopkins Bloomberg School of Public Health.
The course itself uses R to perform data analysis. But since my priority and future objective is to use Python as a general data analysis framework, I decided to follow the course as much as possible using Python. This proved to be a very effective strategy to master data analysis in Python, and more importantly, to know what the limitations are.
Most of data analysis tasks in the course (that are done in R) can be done using the following Python libraries:
And IPython, of course, what else.
Although I discovered as well that for some cases R is the only way to go, for example (incomplete, non-exhaustive list):
The IPython notebooks are created assuming that they are read/executed while watching or following the course video lectures. So it's very likely that you'll find some parts that don't really make much sense if you just read them as it is without the videos. As of now (March 2013) I haven't put so much effort in explaining what is being done in some steps, or what are the objectives of some code snippets. I'm still working on writing more explanations, this is still a work in progress, so stay tuned.