Introduction to Data Science, Machine Learning & AI
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Outline
» Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
» Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events
» Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
» Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
» Use biologically-inspired Neural Networks to learn from observational data as humans do
» Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis
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