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eR_ic

Eric

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ML Internship Prep
Trying to make predictions on data containing some spatial aspect
Spatial Interaction Models in R
Trying to get my footing in SIMs
Natural Language Features: Chapter 3 Stop words (Studying out loud)
Finally trying to get my footing in Predictive modeling for text using the book: Supervised Machine Learning for Text Analysis in R
Train and Evaluate Clustering Models using Tidymodels and friends
This module is a build-up to a previous tutorial that focused on classification using Tidymodels. In this module, we hit the ground running with unsupervised learning. We use the `Tidymodels` framework in `R` and other amazing packages to train and evaluate K-Means clustering as well as Hierarchical clustering.
Train and Evaluate Classification Models using Tidymodels
This module is a build-up to a previous tutorial that focused on regression using Tidymodels. In this module, we go a step further and start building classification models. We use the `Tidymodels` framework in `R` to train and evaluate classification models using different algorithms, do some data preprocessing, tune some hyperparameters and make better predictions.
Natural Language Features: Chapter 2 Tokenization (Studying out loud)
Finally trying to get my footing in Predictive modeling for text using the book: Supervised Machine Learning for Text Analysis in R
Train and Evaluate Regression Models using Tidymodels
This module is a build-up to a previous tutorial that focused on exploring and analyzing data using R. In this module, we go a step further and start building regression models. We use the `Tidymodels` framework in `R` to train and evaluate regression models using different algorithms, do some data preprocessing, tune some hyperparameters and make better predictions.
Exploring and analyzing data with R
Unsurprisingly, the role of a data scientist primarily involves exploring and analyzing data. The results of this analysis might form the basis of a report or a machine learning model; but it all begins with data. In this module, we learn how to use R to explore, visualize, and manipulate data. Data exploration is at the core of data science, and is a key element in data analysis and machine learning.
What we R about when we R about R and Arduino
This post is a gentle introduction to the use of R and Arduino to control peripherals such as LEDs and servo motors. It demonstrates how a communication link can be set up between the two which opens up the possibility of controlling hardware right from R!
A gentle introduction: R in Digital Signal Processing
In this post, we analyze a speech signal in the time and frequency domain whilst covering fundamental concepts in DSP using R.
Tokenization for Natural Language Processing: An R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the eighth YouTube episode of Machine Learning Foundations: Ep 8- Tokenization for Natural Language Processing.
Happy BiRthday Pablo Veramendi
This is just the R community expressing our best wishes to Pablo.
Image augmentation and overfitting: An R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the seventh YouTube episode of Machine Learning Foundations: Ep 7- Image augmentation and overfitting.
Implementing convolutions in R
This Notebook tries to explain the concepts of Convolutional Neural Networks and how they extract features from an image before feeding the output to the Densely Connected layers. Co-authored with Ian Muchiri.
Convolutional cats and dogs: An R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the sixth YouTube session: Machine Learning Foundations: Ep 6- Convolutional cats and dogs
Classifying real-world images: An R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the fifth YouTube session: Machine Learning Foundations: Ep 5- Classifying real-world images.
Improving Computer Vision Accuracy using Convolutions: An R Version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the fourth YouTube session: Machine Learning Foundations: Ep #4 - Coding with Convolutional Neural Networks
Beyond Hello World, A Computer Vision Example: The R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the second YouTube session: Machine Learning Foundations: Ep 2- First steps in computer vision.
The Hello World of Deep Learning with Neural Networks:The R version
This notebook is an R version of the Python notebook used by Laurence Moroney, a Developer Advocate at Google working on Artificial Intelligence, for the first YouTube session: Machine Learning Foundations: Ep #1 - What is ML?
A gentle introduction to Data Wrangling with R
This session aims at demystifying the data wrangling process from importing data to some basic transformation using packages in the Tidyverse.