Machine learning using python syllabus:iisastr

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Machine learning using python syllabus
CLASSIFICATION
• Machine learning basics
• Classifying with k-Nearest Neighbors
• Splitting datasets one feature at a time: decision trees
• Classifying with probability theory: naïve Bayes
• Logistic regression
• Support vector machines
• Improving classification with the AdaBoost meta-algorithm

FORECASTING NUMERIC VALUES WITH REGRESSION
• Predicting numeric values: regression
• Tree-based regression

UNSUPERVISED LEARNING
• Grouping unlabeled items using k-means clustering
• Association analysis with the Apriori algorithm
• Efficiently finding frequent itemsets with FP-growth

ADDITIONAL TOOLS
• Using principal component analysis to simplify data
• Simplifying data with the singular value decomposition
• Big data and MapReduce

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contact Ph: 9312506496
email: info@iisastr.com

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