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This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures 和 dimension reduction, regression models, both linear 和 generalized, KNN 和 Naïve Bayes models, 树模型, 和 SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, 和 implement these methods to solve real-world problems.

This 20-hour Machine Learning with Python course covers all the basic machine learning methods 和 Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple 和 multiple Linear regressions; classification methods including logistic regression, discriminant analysis 和 naive bayes, support vector machines (SVMs) 和 tree based methods; cross-validation 和 feature selection; regularization; principal component analysis (PCA) 和 clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms 和 implement these methods to analyze complex datasets 和 make predictions in Python.

This is a 6-week evening program providing a h和s-on introduction to the Hadoop 和火花 ecosystem of 大数据 technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, 蜂巢, 和火花. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class.