WWCodePython: A Jupyter Notebook repository from Ludim

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	NLP Contextual Word Embeddings Language representations and embeddings are an important step in developing machine learning models for Natural Language Processing. They are used for various language problems - Named entity recognition, POS tagging, sentiment analysis or language understanding and generation. While traditional encodings to convert words and sentences to vectors were more intuitive, static embeddings were developed to incorporate a large vocabulary and corpus and eliminating the problem of sparse vectors. Since then, we’ve seen methods like word2vec, GloVe and now contextual embeddings are the norm - Elmo and now the state-of-the-art BERT. In this talk, we’ll be covering how to create these embeddings with a focus on the most recent development - BERT, using the HuggingFace Transformers Library. We shall also cover how we can obtain BERT multilingual embeddings and how these embeddings are useful in a seq2seq network - which is a specific type of neural architecture to convert sequence of words or characters to another sequence - By Shreya Khurana
	Beyond Unit Tests: End to End Web UI Testing (or Testing google forms, and how much I hate it) Unit tests are great, but they don’t catch all bugs because they don’t test features like a user -- but Web UI tests are complicated & notoriously unreliable. How can we write tests that do the job well? Never fear! 🌟 🌟 🌟 Let’s learn how to write robust, scalable Web UI tests using Python, pytest, & Selenium WebDriver that cover the full stack for any Web app: Using Selenium WebDriver like a pro Modeling Web UI interactions in Python code Writing “good” feature tests that are efficient, robust, and readable Deciding what should and should not be tested with automation In this talk, we'll write one simple test together that covers DuckDuckGo searching. Afterwards, you’ll know how to write battle-hardened Web UI tests for any Web app, including Django & Flask apps. Example code will be supplied on GitHub, as well as plenty of hands-on tutorials & resources to continue learning! - By Andrew Knight
	Programming with Lambda, Map, Filter & Reduce Most of the time, Python is seen as object-oriented -- a style where we model our data in the form of classes, objects, & methods. But Python supports alternatives to that OOP paradigm -- Functional Programming being a very popular choice. Functions like lambda , filter, map, & reduce fully support the Functional Programming style & in this live coding session we'll demonstrate how they can be effectively & efficiently used in our data analysis tasks. Here are some good reads on Functional Programming, to get your started : Don't be Scared of Functional Programming Functional Programming (Python Docs) - By Parul Pandey
	I've Got the Data, Now What? Exploratory Data Analysis(EDA) with Python & Pandas Have you ever started a data science project and not known what to do next? Are you just curious about what a data scientist actually spends most of their time doing? Join us to learn all about Exploratory Data Analysis (EDA) - the process of cleaning, organizing, & understanding your data. During this talk we'll be covering how to import & organize data with pandas, make beautiful visualizations using Seaborn, & use some core Python as well! 🌟 - By Ashley Steele
	ETL Made Simple with PySpark Apache Spark is currently one of the most popular systems for large-scale data processing - making it a standard for any developer or data scientist interested in big data. Spark supports multiple widely used programming languages(Scala, Python, R, Java) and a wealth of built-in and third-party libraries. In Session I you will be introduced to Apache Spark main concepts & you'll learn how to leverage the DataFrame API to extract data. You will also learn how to connect to different sources, apply schemas when reading data, and handle corrupt records. PART I: In session II you will be introduced to some of the most useful transformations - adding new columns, casting column types, renaming columns, etc. You'll also learn how to define User Defined Functions to do your own custom transformations & a get a little introduction to executing your own ad hoc SQL! PART II: In session III you'll analyze the robberies data by doing some aggregations & sorting. You'll learn how to convert Spark DataFrames to Pandas DataFrames. Additionally, you'll explore joins & lookup tables & write final results to CSV files. At the end of this session we'll go over best practices. PART III: - By Aida Martinez
	A Walk-throug of Respiratory Buddy A Web Application helping Doctors & medical personnel detect & diagnose Respiratory/Lung Diseases using CXR (X-Ray data) images. A Strictly Medical Machine-Learning based solution with a special implementation of a CNN (convolutional neural network). - by Vishwa Mehta
	Getting Started with Numpy Python, data, matrices, transformations & all things Numpy! Join Yashika as she covers the basics of this powerful & essential Data Science library that underpins Pandas, Matplotlib, Seaborn, & many many others. - by Yashika Sharma
	✨Emoji Predictor with Machine Learning✨ Python:exclamation:Python:exclamation:Python:exclamation: Let me show you how to use SciKit to do an Emoji 😆 😮 😁 Predictor with a little Machine Learning & Python. 🌈 Learn a few ML & NLP (Natural Language Processing) core concepts ........We do it because we can.:star: - by Alex Gamez, Software Engineer @ Lockheed Martin Aeronautics 🚀
	Data Manipulation with Python Pandas. Come study all the delightful data and play with Pandas! Increase your Python data manipulation skills for fun and profit. - By Liliana Torres
	Titanic Machine Learning webinar 7/30/2019 Do you want to rank in the top 10% in a Kaggle Competition? Explore the Titanic Dataset & get a taste of Exploratory Analysis & Machine Learning Prediction with Python. - By Kunal Lalwani Titanic Icon by Anbileru Adaleru, from The Noun Project. thenounproject.com
	Taller Internacionalización con Django 7/24/2019 Aprende los conceptos relacionados con la internacionalización de aplicaciones web con ejemplo traducción del contenido estatico y dinamico de un sitio web. - Por Isabel Cristina Ruiz Buritica
	Using Jupyter, Pandas & Matplotlib: PyCon 2019 (re-broadcast 7/02/2019) Using Jupyter Notebook, Pandas, and Matplotlib to create a framework for teaching data science in a scientific context. Download the presentation and resources here. - By Gabrielle Rabinowitz
	ML Mondays Join us alternating Mondays for a wholesome & healthy dose of ML 🌟. -- Starting off with a whirlwind review of Python & then diving into foundational libraries. We'll also be discussing the ideas behind ML and covering a little ✨math & statistics✨🎉. As we journey further along, we'll collaborate & help one another with projects & other fun 🔥 stuff. - By Yashika Sharma
	Automate Boring Stuff Automate the Boring Stuff With Python: A friendly forum where we can ask questions, provide feedback & help each other with our coding journey. Explore our code & notes for each chapter here! - By Jamila Evilsizor

Ludim/WWCodePython

Clicking on a logo below will take you to the associated repo -- where you can review, copy or clone the repos content. Please reach out with any questions or issues, or join us in our Slack channel for interactive discussions and support.