Amazon Machine Learning – Introduction

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. With rise of big data machine learning become a key technique for solving problems.

Machine learning uses two types of techniques:

  • Unsupervised
    • Used for unlabeled data and where we don’t know the output.
    • Self guided learning algorithm
    • Clustering Technique: Aim is to use exploratory data analysis to find hidden patterns or groupings in data.
  • Supervised
    • Used for labelled data and desired output is known.
    • providing the algorithm training data to learn from
    • Techniques available:  Classification and Regression

Amazon Machine Learning:

Amazon ML is a robust machine learning platform that allow developers to train predictive models. Amazon ML creates models from supervised data sets. The process of creating a model from set of known observation called training data. When setting up a new model in Amazon ML, we first need to upload our data. Data needs to be CSV-formatted, with the first row containing the name of each data field, and each following row containing the data samples. Training data sets can be huge, so they need to be uploaded from either Amazon S3 or Redshift storage.

To test the amazon ML, I uploaded the two datasets to S3. I used customer review data to predict whether customer will like the restaurant or not. And second one is to predict House pricing based on previous sale. Continue reading → Amazon Machine Learning – Introduction