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info-201-group-project

Group Project by TEAM#1

info-homework

Collection of my old Info 201 HW Assignments, beginner mistakes and all

info201

Projects relating to the class "Technical Foundations of Informatics"

info201-2

info201-3

info201-4

info201-5

Project and assignments for INFO 201 at UW Seattle

info201-6

Using R to analyze various datasets and come up with resulting findings.

info201-7

info201-a6

info201_hw4

info_201

Includes homework/assignment folders for R programming class

intermediate-python---processing-csvs

intro-java-programming

learnr

A tutorial for R programming language at basic and advanced levels, and its application in data transformation, statistical computing, time series analysis, data mining, financial computing, etc.

m14-shiny

Module 14: Shiny

machine-learning-a-z-udemy

All the codes in Python and R for the course Machine Learning A-Z on Udemy

machine-learning-specialization

machine-learning-university-of-washington

Assignments for University of Washington Machine Learning Specialization through Coursera

machinelearningcasestudies

Following the course of Machine Learning Foundation--- Case Study Approach by Washington University, I replicate all the cases used in the course by R, Sklearn and Graphlab (R is not possible for some cases). Course Link: https://www.coursera.org/learn/ml-foundations/home/welcome

machinelearninginaction

Source Code for the book: Machine Learning in Action published by Manning

managing-big-data-with-mysql

mathstat394aut2023

Course materials for MATH/STAT 394

mlusingr

ml_foundations-uw-assignments

Machine Learning applications done using Ipython Notebooks

natural-language-processing

Resources for "Natural Language Processing" Coursera course.

newdata

Starter Data Science

pandas

pandas is a Python library for data analysis. It offers a number of data exploration, cleaning and transformation operations that are critical in working with data in Python. pandas build upon numpy and scipy providing easy-to-use data structures and data manipulation functions with integrated indexing. The main data structures pandas provides are Series and DataFrames. After a brief introduction to these two data structures and data ingestion, the key features of pandas this notebook covers are: Generating descriptive statistics on data Data cleaning using built in pandas functions Frequent data operations for subsetting, filtering, insertion, deletion and aggregation of data Merging multiple datasets using dataframes Working with timestamps and time-series data

practical-time-series-analysis

Practical Time-Series Analysis, published by Packt

practice-it

This is for online practice questions from http://practiceit.cs.washington.edu/practiceit/index.jsp