https://github.com/mauropelucchi/machine-learning-course
Machine Learning and Deep Learning Course
https://github.com/mauropelucchi/machine-learning-course
als artificial-intelligence clustering-algorithm deep-learning ensemble-learning glm h2o kaggle machine-learning r random-forest scala spark supervised-learning text-classification text-mining topic-modeling unsupervised-learning word2vec xgboost
Last synced: 6 months ago
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Machine Learning and Deep Learning Course
- Host: GitHub
- URL: https://github.com/mauropelucchi/machine-learning-course
- Owner: mauropelucchi
- License: mit
- Created: 2017-09-22T14:27:20.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2019-02-18T08:07:37.000Z (over 6 years ago)
- Last Synced: 2025-03-28T17:57:30.690Z (7 months ago)
- Topics: als, artificial-intelligence, clustering-algorithm, deep-learning, ensemble-learning, glm, h2o, kaggle, machine-learning, r, random-forest, scala, spark, supervised-learning, text-classification, text-mining, topic-modeling, unsupervised-learning, word2vec, xgboost
- Language: R
- Homepage:
- Size: 11.8 MB
- Stars: 8
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning and Deep Learning Course
This repository contains a topic list of Machine Learning and Deep Learning tutorials, articles and other resources.
If you want to contribute to this repository (please do!!!), send me a pull request and an email (mauro.pelucchi @ gmail.com).
Since I want these documents to be useful in the long run, please submit high quality materials or distintive researchs.
For any info please don't exit to contact me at mauro.pelucchi@gmail.com
# Topic list
- [Recommending Music and the Audioscrobbler Data Set](https://github.com/mauropelucchi/machine-learning-course/blob/master/collaborative-filtering/recommending_music.scala)
- [Forest Cover type Data Set with Decision Trees and Random Forest](https://github.com/mauropelucchi/machine-learning-course/blob/master/random-forest/covtype_rdf.scala)
- [KDD 1999 CUP - Anomaly Detection in Network Traffic by KMeans](https://github.com/mauropelucchi/machine-learning-course/blob/master/kmeans/kdd1999_kmeans.scala)
- [Instacart Market Basket Analysis from Kaggle](https://github.com/mauropelucchi/machine-learning-course/blob/master/xgboost/h2o_instacart.r)
- [A novel story from exploration analysis of toxic comments: train a Word2Vec model](https://github.com/mauropelucchi/machine-learning-course/blob/master/text-mining/h2o_toxic_exploration.r)
- [GBM, GLM, Naive Bayes and Deep Learning for classify Toxic comments (H2O and R)](https://github.com/mauropelucchi/machine-learning-course/blob/master/text-mining/h2o_toxic_comments.r)
- [GeoPySpark Demo (GeoTrellis + PySpark)](https://github.com/mauropelucchi/machine-learning-course/tree/master/geopyspark)
- [GeoSpark Demo on Regione Lombardia Open Dataset - Meteo](https://github.com/mauropelucchi/machine-learning-course/tree/master/geospark)
- [TensorFlow - Deep Learning on MNIST Dataset - CNN](https://github.com/mauropelucchi/machine-learning-course/tree/master/mnist)
- [H2O KNN Clustering on Uber Dataset (Kaggle)](https://github.com/mauropelucchi/machine-learning-course/tree/master/kmeans_uber)
- [SparkR Demo on Uber Dataset](https://github.com/mauropelucchi/machine-learning-course/tree/master/uber_dataset)
- [Netatmo analysis with MongoDB Aggregate Pipeline and Spatial Function](https://github.com/mauropelucchi/machine-learning-course/tree/master/netatmo-dataset)
# MIT License
Copyright (c) 2017 Mauro Pelucchi
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.