6.Machine Learning with Big Data (Spark MLlib ) Data types Basic statistics summary statistics correlations stratified sampling hypothesis testing streaming significance testing random data generation Classification and regression linear models (SVMs, logistic regression, linear regression) naive Bayes decision trees ensembles of trees (Random Forests and Gradient-Boosted Trees) isotonic regression Collaborative filtering alternating least squares (ALS) Clustering k-means Gaussian mixture power iteration clustering (PIC) latent Dirichlet allocation (LDA) bisecting k-means streaming k-means Dimensionality reduction singular value decomposition (SVD) principal component analysis (PCA) Feature extraction and transformation Frequent pattern mining FP-growth association rules PrefixSpan Evaluation metrics PMML model export Optimization (developer) stochastic gradient descent limited-memory BFGS (L-BFGS) Pipelines Model selection and tuning
7.Deep Learning with Big Data Basic Deep Learning Advanced Deep Learning Available Deep Learning Platforms
