Data Mining | Machine Learning | Spark | Stanford University

Explore large-scale data mining and machine learning techniques using Spark. Gain practical skills for processing massive datasets at Stanford University.

Introduction

The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on MapReduce and Spark as tools for creating parallel algorithms that can process very large amounts of data.

Highlights

• Covers topics such as Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommendation Systems, Clustering, Link Analysis, Large-scale Supervised Machine Learning, Data streams, Mining the Web for Structured Data, Web Advertising.
• Provides hands-on experience with Spark through Colab notebooks.
• Requires strong background in computer science, probability, linear algebra, and algorithmic analysis.

Recommendation

This course is recommended for students interested in large-scale data mining and machine learning, especially those with a strong computer science and quantitative background. It provides practical skills in using Spark for processing massive datasets.