https://github.com/manuelseeger/nd-machine-learning

Projects from Udacity's Machine Learning Nanodegree
https://github.com/manuelseeger/nd-machine-learning

Last synced: 23 days ago
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Projects from Udacity's Machine Learning Nanodegree

• Host: GitHub
• URL: https://github.com/manuelseeger/nd-machine-learning
• Owner: manuelseeger
• Created: 2019-07-13T15:14:20.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-22T01:40:01.000Z (about 2 years ago)
• Last Synced: 2023-03-03T13:41:14.928Z (almost 2 years ago)
• Language: Jupyter Notebook
• Size: 14.2 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# nd-machine-learning

My completed projects from Udacity's Machine Learning Engineer Nanodegree

My Nanodegree certificate: https://graduation.udacity.com/confirm/EGH7NNKR

## Capstone: Predicting customer churn

Directory: [capstone](capstone)

Predicting churning customers in the telco industry from customer demographic, financial, and usage data.

* Data quality analysis

* Exploratory data analysis

* Data cleaning / data transformation

* Model building and model selection

* Model performance evaluation 

## Deep Learning

Directory: [dog_project](dog_project)

Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

* Deep learning

* Convolutional neural nets

* Transfer learning

* Keras / tensorflow

## Reinforcement Learning

Repository: https://github.com/manuelseeger/RL-Quadcopter-2

Teach a quadcopter how to fly with reinforcement learning. Includes implementations of DDPG agents for the quadcopter environment and the mountaincar-continuous environment.   

* Deep reinforcement learning

* Actor-critic methods

* Deep Q-learning, DDPG

* Experiment design and management

## Unsupervised learning

Directory: [customer_segments](customer_segments)

Customer segmentation based on buying behavior. Segment customers into clusters based on their purchases.

* Data transformation, outlier removal 

* Clustering: KNN, Gaussian mixture models

* Principal component analysis

## Supervised learning

Directory: [finding_donors](finding_donors)

* Data quality analysis

* Exploratory data analysis

* Data cleaning / data transformation

* Model building and model selection

* Model performance evaluation