oursera Data Science Capstone Project. This app is based on NLP word prediction model, similar to swiftkey. It used web text data from blogs, twitter, news, cleans them, uses in building N-gram models, that is used for next word prediction using stupid back-off strategy.

The goal of this report is to provide a quick update that the work on the data has progressed and on track to create prediction algorithm. This report explains the exploratory analysis and goals for the eventual app and algorithm.

We will step through a deep learning framework that will allow us to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Keras, and many others can speed up machine learning development significantly. All of these frameworks also have a lot of documentation, which helps in understanding and deploying them.

Access tensorflow with Keras package in R notes

wageapp is built thru shiny to produce visualization of relationship between various features and wage, from the famous Wage data from ISLR package. a cleaner version of the presentation.

Create a web page presentation using R Markdown that features a plot created with Plotly. webpage must contain the date that you created the document, and it must contain a plot created with Plotly.

Create a web page using R Markdown that features a map created with Leaflet. Host your webpage. Your webpage must contain the date that you created the document, and it must contain a map created with Leaflet. We would love to see you show off your creativity!

Coursera Practical Machine Learning Course Project Submission. Topic is learning from fitness tracker data.

Part 2: Basic Inferential Data Analysis Now in the second portion of the project, we're going to analyze the ToothGrowth data in the R datasets package. Load the ToothGrowth data and perform some basic exploratory data analyses Provide a basic summary of the data. Use confidence intervals and/or hypothesis tests to compare tooth growth by supp and dose. (Only use the techniques from class, even if there's other approaches worth considering) State your conclusions and the assumptions needed for your conclusions.

Exponential Distribution Compared with Central Limit Theorem through Simulation

This project involves exploring the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database.