arcada-uas Goto Github PK
Name: Arcada UAS
Type: Organization
Name: Arcada UAS
Type: Organization
Perform DataFrame operations in Pandas for a more in-depth look at data wrangling in practice
Explore more advanced data structures in Python and how to handle them with some basic file operations
This module covers the implementation of advanced RNN models that overcome the drawbacks of plain RNNs. We will particularly look at LSTM, GRU-based model, Bi-directional and Stacked RNNs.
Decode responses and extract text from the Request and BeautifulSoup libraries, read and scrape data from XML files, and implement regular expressions to practice advanced web scraping on APIs.
This module enables you summarize and identify the quality of the data using concepts such as aggregation and window functions.
Learn the fundamental concepts of data wrangling and statistics, and understand how they relate to data visualization.
Anafora is a web-based raw text annotation tool
This module covers performing descriptive analytics on time series data, geospatial data, complex data types (arrays, JSON, and JSONB), and text.
In this module you will look at AWS AI services and examine an emerging computing paradigm – the Serverless Computing. We will then proceed to applying NLP and the Amazon Comprehend service to analyze documents.
Analyze marketing campaign data related to new financial products. Discover linear and logistic regression models, and explore the relationships between the different features in the data
Identify missing values, outliers and trends in medical data. Create bar charts, heatmaps and other visualizations to understand how the features impact the target column of the data set
Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data
Fix, clean, merge, and connect new data to perform data wrangling tasks on UN and GDP data
This module covers probability theory and looks at how you can use NumPy and SciPy to solve probability problems.
Web Pages
An AI-powered Python notebook built in React — generate and edit code cells, automatically fix errors, and chat with your code
This course will take a look at autoencoders and their applications will help you see how autoencoders are used in dimensionality reduction and denoising. You will implement an artificial neural network and an autoencoder using the Keras framework. By the end of this course, you will be able to implement an autoencoder model using convolutional neural networks.
Python and Linux prerequisite knowledge requirements for BDA specialization program.
Discover what it means to be "Pythonic", learn to write succinct, readable expressions for creating lists; use Python comprehensions with lists, dictionaries, and sets.
This module covers derivatives and integrals and how Python can be used to perform basic calculus.
This repository contains code for bridging resolution and its sub-tasks (i.e., bridging anaphora recognition and bridging anaphora resolution or antecedent selection for bridging anaphors).
This module will cover the key stages involved in building a comprehensive program. It also explains how to build and save a model such that you get the same results every time it is run and call a saved model to use it for predictions on unseen data.
Learn how to build a machine learning mode and get started on the popular deep learning framework PyTorch. You will delve into one of the most exciting fields in deep learning research - reinforcement learning - and take a closer look at the deep Q-learning algorithm
Review the mathematics that comprise Artificial Neural Networks, apply linear transformations in Python, and build a logistic regression model with Keras
This module provides you with a good understanding what deep learning is and how programming with TensorFlow works
Cataclysm - Code generation library for the end game
CherryPy is a pythonic, object-oriented HTTP framework. https://docs.cherrypy.org/
Citations in the Jupyter Notebook
This module covers the concept of clustering in machine learning. It explains three of the most common clustering algorithms, with a hands-on approximation to solve a real-life data problem. The three clustering algorithms covered are k-means, mean-shift and DBSCAN algorithms.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.