| My Machine Learning Cookbook - Jeffrey Long |
Machine Learning in R with tidymodels, {parsnip}, {tidymodels}, {broom.mixed}, {rstan}, {skimr}, {yardstick} |
jeffreyCarlLong GitHub R Vignette |
| {tidymodels} |
Hadley Wickham brings ML to R tidy() |
tidy package |
| {broom} |
Tidies 100+ models from popular modeling packages and almost all of the model objects in the stats package that comes with base R, tidy(), glance(), augement(), vignette("broom") |
broom package |
| {recipes} |
Feature engineering steps to process data, step_date(), step_holiday(), step_rm(), convert indicator variables to one hot encoding |
recipes package |
| {workflows} |
pairs a model and a recipe together workflow(), add_model(), add_recipe() |
|
| {rsample} |
Data splitting to create training and testing sets, initial_split(), training(), testing() |
rsample package |
| {rstanarm} |
Bayesian prior distributions for rstanarm models, stan_glm(), prior, prior_intercept, linear_reg |
rstanarm package |
| {parsnip} |
Train models with different engines, model type (random forests, linear regression, LSVM), mode (classification, regression), computational engine (R packages, methods), set_engine() |
parsnip package, parsnip models |
| {yardstick} |
ROC curves, predicted model metrics, roc_curve() and roc_auc() |
yardstick package |
| easystats: Quickly investigate model performance |
Inspiration to learn tidymodels |
R Bloggers |
| Mixing centered and non-centered parameterizations in a hierarchical model with PyMC3 |
Hierarchical models |
Joshua Cook |
| Meetup slides: Introducing Deep Learning with Keras |
General Keras slides |
Shirin's playgRound |
| How to call bullshit on AI companies (aka a short lesson on recall) |
precision, recall, accuracy |
Cartesian Faith |
| ML models: What they can’t learn? |
True model plots |
R Bloggers |
| Automated Feature Selection Using bounceR |
GitHub Repo |
Statworx |
| Machine Learning Yearning |
Andrew Ng |
Book Chapters 1-14 |
| Machine Learning Crash Course |
TensorFlow APIs |
Google Course |
| Machine Learning Modeling in R |
Cheat sheet |
The R Trader |
| scikit-learn Clustering |
sklearn metrics datasets numpy clustering |
Documentation |
| TensorFlow for Poets |
Python Notebook for ML |
Gist |
| Google Calab |
Train your ML Model 4 FREE |
Upload Python Notebook |
| Train Your Machine Learning Models on Google’s GPUs for Free — Forever |
Google Collab |
Hackernoon |
| 15 Types of Regression You Should Know |
Stats, fit code, ggPlots |
Listen Data |
| TensorFlow for R |
Slides and Book recommendations |
R-bloggers |
| Fitting a TensorFlow Linear Classifier with tfestimators |
TensorBoard visualization tool, Titanic data set |
R-bloggers |
| TensorFlow |
Machine Learning API from Google |
TensorFlow Basics |
| MNIST |
Machine learning with TensorFlow |
MNIST TensorFlow |
| What's the difference between data science, machine learning, and artificial intelligence? |
Good explanation in lay language |
David Robertson's Blog- Variance Explained |
| Building a neural network from scratch in R |
Is it a hot dog? |
Tea & Stats, data science with David Selby |
| Deep Learning from first principles in Python, R and Octave |
Decision boundary with hidden units i and learning rate j |
Giga Thoughts Part I, and Part II |
