#today_tip All 33 keywords in Python
@epythonlab

180 Data Science and Machine Learning Projects with Python

https://medium.com/coders-camp/180-data-science-and-machine-learning-projects-with-python-6191bc7b9db9

@epythonlab

Machine Learning and Data Science Blueprints for Finance (2020)

@epythonlab #mlbooks

According to the 2020 Kaggle ML and DS survey, 80% of data scientists first learn Python. Why?

Watch "Why Python is so Popular?" on YouTube
https://youtu.be/hrOGa8XXPrk

Python for bioinformatics, second edition

@epythonlab #pythonbooks

Function definition in Python
https://youtu.be/5IHXsnxdVFI

The pre-requisite topics that you should understand before you get started Data science and Machine Learning.

Mathematics:-

- covering differential calculus,
- integral calculus,
- matrices and linear algebra,
- set theory,
- differential equations,
- complex numbers,
- sequences and series,
- logic and proofs,
- multivariate calculus,
- convergence of sequences and functions,
- and partial differential equations.

Statistics:-

- covering exploratory data analysis,
- elementary probability,
- univariate random variables,
- bi-variate random variables,
- generating functions,
- statistical estimation theory,
- hypothesis testing and confidence intervals,
- and simple linear regression.

#DataScience #machinelearning #statistics #mathematics @epythonlab

Here is a useful website to learn mathematics for free
https://www.britannica.com/browse/Mathematics

Open Datasets for Research

During last week there were several news about newly open datasets for researchers.

1. Twitter opened “full history of public conversation” for academics (specifically, for academics):
https://www.theverge.com/2021/1/26/22250203/twitter-academic-research-public-tweet-archive-free-access
We can happily conduct researches about social networks graphs, users behavior and fake news (especially fake news🙃) without fighting with Twitter API.

2. Papers with code are now also Papers with Datasets:
https://www.paperswithcode.com/datasets
Not for only NLP, but for all fields structured for easy search and download.

#Keynote #DataScience #NLP #Python @epythonlab

Natural Language Processing

Natural language processing (NLP) is the field devoted to methods and algorithms for processing human (natural) languages for computers. NLP is a vast discipline that is actively being researched. Some examples of machine learning applications using NLP include sentiment analysis, topic modeling, and language translation. In NLP, the following terms have specific meanings:

- Corpus: The body/collection of text being investigated.
- Document: The unit of analysis, what is considered a single observation.

Examples of corpora include a collection of reviews and tweets, the text of the Iliad, and Wikipedia articles. Documents can be whatever you decided, it is what your model will consider an observation. For the example when the corpus is a collection of reviews or tweets, it is logical to make the document a single review or tweet. For the example of the text of the Iliad, we can set the document size to a sentence or a paragraph. The choice of document size will be influenced by the size of our corpus. If it is large, it may make sense to call each paragraph a document. As is usually the case, some design choices that need to be made.

spaCy is a Python package that bills itself as "industrial-strength" natural language processing.
https://spacy.io/

Watch "Python Tutorial Part-09| Function Definition in Python" on YouTube
https://youtu.be/5IHXsnxdVFI

ML Algorithms Cheatsheet (python and R)
#code #python #R
@epythonlab #mlbooks

#nlp #ml @epythonlab

#nlp #ml @epythonlab

Function Scope: Global or Local
https://www.youtube.com/watch?v=GzbPCRcf-gU