Amaryllis's repositories
-Supervised-Machine-Learning
(Supervised) Machine Learning Instructor: Paul Clough (paul.clough@peakindicators.com | p.d.clough@sheffield.ac.uk) This session will introduce libraries and functions in R for performing Machine Learning (ML). Machine Learning is typically viewed as a sub-field within Artificial Intelligence (AI): “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” (Oxford English Dictionary, 2018). The most common forms of ML are: (i) supervised learning (e.g., prediction/regression and classification); (ii) unsupervised learning (e.g., clustering); and (iii) reinforcement learning. This session will focus on supervised ML. We will start by reviewing linear regression that forms a good starting point for understanding machine learning and may be something you are already familiar with. Then we will explore further algorithms that can be used for regression and classification. We will mainly focus on using the caret package for ML, but (as usual) there are many ways of doing things in R and multiple packages that can be used for ML1 . Note: this session is very much a hands-on overview of supervised machine learning and some of the R functions that can be used. For a more theoretical overview you might find the book “An Introduction to Statistical Learning with Applications in R” and the accompanying videos helpful2
Deep-learning-
Deep learning models are capable of solving problems once thought unapproachable. The workshop by Vanderbilt Data Science Institute cover basic theory and frameworks to train your own. How does deep learning work, in theory? How do you go about building a model? What are the skills and background you need to train a model, develop a new model, devise an new architecture, or truly understand how these modesl learn? This workshop cover a high-level introduction, and end with suggestions and resources for next steps.
Intro-to-Data-Analytics-SQL-Fundamentals
SQL (Structured Query Language) is the most ubiquitous language for data scientists and data analysts. Companies store their data in a database, and SQL is a common database query language that helps you to build your database, retrieve the data, and work with that data in a structured manner. In this course, you’ll learn to extract and analyze data stored in databases. You’ll first learn to extract data and work with simple queries to pull specific data you need for your analysis. Join us for a fun interactive workshop that will get you on your way to using SQL.
-Intro-to-Computer-ision-Building-Object-Detection-Models-and-Datasets-
Hands-on workshop to get started with computer vision and object detection. Build your own object detection model from start to finish. Includes step-by-step instructions on data annotation and model training with your own dataset. Object classification and localization within an image is foundational to many computer vision applications. In this workshop we'll cover: High level computer vision applications & concepts How to label your own dataset for object detection & computer vision How to train your model using python & detectron2 (A PyTorch based modular object detection library)
Akka
AKKA TECHNOLOGIES As a global leader in engineering consulting and R&D services, AKKA supports the world's leading industry players in their digital transformation and throughout their entire product life cycle. Let's analyze AKKA's stocks and learn how to use Kaleido to generate PDF documents.
Amaryllis2021
Welcome!
Analysis_using_Python_Workshop_WIMTACH_CentennialCollege-
Detailed AGENDA of the workshop 1. Discuss the needs and applications of data analytics in the healthcare industry and give a description of the most recent trends in this field; Introduce Python, its features and use for data preprocessing and analyses. Explain Python libraries (Pandas, Numpy, Scipy, Statsmodels, Scikit-Learn, Matplotlib, Seaborn, Scrapy etc.) and their applications (1 hour). 2. Break (10 minutes). 3. Demonstration: Installing Python, Loading/importing data (CSV, Excel), selecting and filtering data, deleting columns, data cleaning, sorting, merging etc.(1.15 hours). 4. Break (10 minutes) 5. Demonstration continues Basic visualizations (Bar chart, Line chart, Scatter plot etc.)., and basic analysis: measures of central tendency(mean, median, mode)Correlation, Chi-Square and t-test. (1.15 hour). 7. Questions and Answers (10 minutes).
Animated-Visualizations-with-R
Animated Visualizations with R | 1. Covid-19 Time series Plot
Awesome-Profile-README-templates
A collection of awesome readme templates to display on your profile
Brock-University-DS-Lab-Text-Analysis-with-Python
Introduction to Python! Welcome to the Digital Scholarship Lab introduction to Python class. By the end of today we'll know all about the following; variables math conditional loops functions We'll also do some basic text analysis (if time allows) We'll use the Zoom's chat feature to interact. Be sure to enable line numbers by looking for the 'gear' icon and checking the box in the 'Editor' panel.
datasciencecoursera-
Assignments
Credit-Card-Fraud-Detection
This project aims to detect fraudulent credit card transactions using machine learning algorithms. The dataset used in this project contains more than 2000 transactions, of which 2% were found to be fraudulent and 98% were found to be genuine.
datasharing
The Leek group guide to data sharing
l10n-guide
Localisation guide
Lisbon-Market-Share-Analysis
This project aims to analyze the market share of different companies operating in Portugal. The goal is to understand the current state of the market, identify trends and patterns, and provide insights for strategic decision making.
Microsoft-Azure-Fundamentals-AZ900-
This course will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure.You will Learn After completing this course, students will be able to: The basics of cloud computing and Azure, and how to get started with Azure's subscriptions and accounts. The advantages of using cloud computing services, learning to differentiate between the categories and types of cloud computing, and how to examine the various concepts, resources, and terminology that are necessary to work with Azure architecture. The core services available with Microsoft Azure. The core solutions that encompass a wide array of tools and services from Microsoft Azure. The general security and network security features, and how you can use the various Azure services to help ensure that your cloud resources are safe, secure, and trusted. The identity, governance, privacy, and compliance features, and how Azure can help you secure access to cloud resources, what it means to build a cloud governance strategy, and how Azure adheres to common regulatory and compliance standards. The factors that influence cost, tools you can use to help estimate and manage your cloud spend, and how Azure's service-level agreements (SLAs) can impact your application design decisions.
NLP
Natural Language Processing (NLP) is revolutionizing the way we communicate, make decisions, and more! There is a huge demand for people with skills in natural language processing: The market has forecasted growth of 20% through 2024 and is expected to have a market size of 23 Billion USD. This workshop is a perfect gateway to gaining a fundamental understanding of how to leverage natural language processing techniques in Python to analyze text and make decisions. If you have a background in Data Science, Data, Software, or Machine Learning Engineering this workshop would be a great way to either get an introduction to the field or refresh an area you have worked with in the past. It doesn't matter if you are coming from a large or small company, startup, or even if you're creating a custom app- NLP can help you design new products and services or better leverage the data you may already have. We strongly recommend that you are comfortable with using the Python programming language and have had exposure to the following: Importing libraries Using variables Using methods Interacting with Pandas DataFrames What You Will Learn: Understanding NLP as a field How to acquire data Ability to Isolate the text Ability to tokenizing text Ability to remove stopwords Ability to stem or lemmatize text Representing text as a matrix Be able to compare documents using similarity measures Taught live online: - 2.5 Hours – 5:30pm - 8:00pm Pacific Time
ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
Roadmap_Python
This roadmap specifically covers Python and the ecosystem around it.
SQL4DataScientists
SQL, or Structured Query Language, is a powerful tool for data scientists who work with large datasets.
Thinkful_Free_Crash_Course_Natural_Language_Processing
Information and practical exercises to add to your current toolkit or take the first step in launching a new career.
translate-project
The Translate Project