arcada-uas Goto Github PK

a-deep-dive-into-data-wrangling-with-python

Perform DataFrame operations in Pandas for a more in-depth look at data wrangling in practice

advanced-operations-on-python-data-structures

Explore more advanced data structures in Python and how to handle them with some basic file operations

advanced-rnns

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.

advanced-web-scraping-and-data-gathering

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.

aggregate-and-window-functions

This module enables you summarize and identify the quality of the data using concepts such as aggregation and window functions.

an-introduction-to-data-visualization-and-data-exploration-with-python

Learn the fundamental concepts of data wrangling and statistics, and understand how they relate to data visualization.

anafora

Anafora is a web-based raw text annotation tool

analytics-using-complex-data-types

This module covers performing descriptive analytics on time series data, geospatial data, complex data types (arrays, JSON, and JSONB), and text.

analyzing-documents-and-text-with-natural-language-processing

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.

analyzing-the-bank-marketing-dataset

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

analyzing-the-heart-disease-dataset

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

analyzing-the-sequence-of-data-with-rnns-using-pytorch

Understand the learning process of RNNs and discover the LSTM network architecture. Solve problems and perform Natural Language Processing using sequences of data

applications-in-business-use-cases

Fix, clean, merge, and connect new data to perform data wrangling tasks on UN and GDP data

applying-foundational-probability-concepts

This module covers probability theory and looks at how you can use NumPy and SciPy to solve probability problems.

arcada-uas.github.io

Web Pages

arcada_puhti_demo

atactl-thread

An AI-powered Python notebook built in React — generate and edit code cells, automatically fix errors, and chat with your code

autoencoders

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.

bda-spec-requirements

Python and Linux prerequisite knowledge requirements for BDA specialization program.

becoming-pythonic

Discover what it means to be "Pythonic", learn to write succinct, readable expressions for creating lists; use Python comprehensions with lists, dictionaries, and sets.

beginning-calculus-with-python

This module covers derivatives and integrals and how Python can be used to perform basic calculus.

bridging-resolution

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).

building-a-trained-model

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.

building-an-artificial-intelligence-algorithm

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

building-artificial-neural-networks-in-keras

Review the mathematics that comprise Artificial Neural Networks, apply linear transformations in Python, and build a logistic regression model with Keras

building-blocks-of-deep-learning

This module provides you with a good understanding what deep learning is and how programming with TensorFlow works

cataclysm

Cataclysm - Code generation library for the end game

cherrypy

CherryPy is a pythonic, object-oriented HTTP framework. https://docs.cherrypy.org/

cite2c

Citations in the Jupyter Notebook

clustering

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.