This repository serves as a portfolio for the projects I completed for Udacity's Machine Learning Nanodegree by Google.

Learn more on the wiki! https://github.com/hsherwoodcoombs/Machine-Learning/wiki

1. Model Evaluation and Validation

Apply statistical analysis and tools to model observed data and gauge how well your models perform

• Measure of central tendency
  • Veritability of data
  • Numpy & Pandas Tutorial
  • Scikit-learn tutorial
  • Evaluation metrics
  • Causes of error
  • Nature of data and model building
  • Training and testing
  • Cross validation
  • Representative Power of a Model
  • Learning Curves and Model Complexity Project: Predicting Boston Housing Prices

2. Supervised Learning

Learn how Supervised Learning models such as Decision Trees, SVMs, Neural Networks, etc. are trained to model and predict labeled data.

• Supervised learning background

  • Regression
  • Classification

  Decision Trees

  • Neural Networks
  • Mini Project: Build a perceptron

  Support Vector Machines

  • Kernel Methods & SVMs
  • SVM

  Nonparametric Models

  • Instance Based Learning

  Bayesian Methods

  • Naive Bayes
  • Bayesian Learning
  • Bayesian Inference
  • Bayes NLP Mini Project

  Ensemble of Learners

  • Ensemble B&B

  Project: Building a Student Intervention System

3. Unsupervised Learning

   Unsupervised Learning

   • Clustering
   • Feature Scaling
   • Feature Selection
   • PCA

   Mini-Projects

   • Clustering mini-project
   • PCA mini-project

   Project: Creating Customer Segments

   • Naive Bayes Classifier
   • Support Vector Machines
   • Decision Trees

4. Reinforcement Learning

Use Reinforcement Learning algorithms like Q-Learning to train artificial agents to take optimal actions in an environment.

• Introduction to reinforcement learning

  • Markov decision processes (MVPs)
  • Game theory

  Project: Train a smart cab to drive

This project requires Python 2.7 and the following Python libraries installed:

NumPy Pandas matplotlib scikit-learn You will also need to have software installed to run and execute an iPython Notebook

Udacity recommends our students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

This will open the iPython Notebook software and project file in your browser.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Please refer to Udacity Terms of Service for further information.