preface chapter 1: basic python and linear algebra for predictive analytics a basic introduction to predictive analytics why predictive analytics? working principles of a predictive model a bit of linear algebra programming linear algebra installing and getting started with python installing on windows installing python on linux installing and upgra pip (or pip3) installing python on mac os installing packages in python getting started with python python data types using strings in python using lists in python using tuples in python using dictionary in python using sets in python functions in python classes in python vectors, matrices, and graphs vectors matrices matrix addition matrix subtraction fin the determinant of a matrix fin the transe of a matrix solving simultaneous linear equations eigenvalues and eigenvectors span and linear independence principal ponent analysis singular value deition data pression in a predictive model using svd predictive analytics tools in python summary chapter 2: statistics, probability, and information theory for predictive modeling using statistics in predictive modeling statistical models parametric versus nonparametric model population and sample random sampling expectation central limit theorem skewness and data distribution standard deviation and variance covariance and correlation interquartile, range, and quartiles hypothesis testing chi-square tests chi-square independence test basic probability for predictive modeling probability and the random variables generating random numbers and setting the seed probability distributions marginal probability conditional probability the chain rule of conditional probability independence and conditional independence bayes rule using information theory in predictive modeling self-information mutual information entropy shannon entropy joint entropy conditional entropy information gain using information theory …… chapter 3: from data to decisions - getting started with tensorflow chapter 4: putting data in ce -supervised learning for predictive analvtics chapter 5: clustering your data - unsupervised learning for predictive analytics chapter 6: predictive analytics pipelines for nlp chapter 7: using deep neural works for predictive analytics chapter 8: using convolutional neural works for predictive analvtics chapter 9: using recurrent neural works for predictive analytics chapter 10: remendation systems for predictive analytics chapter 11: using reinforcement learning for predictive analytics index
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