Prep for data science interviews

Key Subjects Conditional Random Fields Gaussian Process Gradient Boosted Trees

• Elasticsearch Hidden Markov Models Hierarchical Clustering Information Retrieval Algorithms K-Means K Nearest Neighbors Kalman Filter Latent Dirichlet Allocation Latent Semantic Indexing Linear Regression Logistic Regression Markov Process Maximum Entropy Markov Models Mixture Models Monte Carlo Simulation Multinomial Logistic Regression Neural Networks Random Forests Random Walk Restricted Boltzmann Machine Smith-Waterman Alignment SVD, A-SVD, SVD++, HOSVD SVM, one class-SVM Spectral Clustering TF-IDF scoring Vector Space Model