Generalizing from Data-Rectangular Data-Relational Databases and SQL- Indexes, Slicing, Sorting- Applying & Plotting Data science Process.
Data Representation-Data Quality-Exploratory Data Analysis-Data Visualization-Text Mining -Text Analytics.
Working with Text-Regular Expressions-Web Technologies - REST - Xpath - Handling large Data on a Single Computer -Applications for Machine Learning in Data Science-Introducing Naive Bayes Classifiers-The Rise of graph databases
Regression on Probabilities-The Logistic Model-A Loss Function for the Logistic Model-Fitting the Logistic Model-Evaluating the Logistic Models- Multiclass Classification-Data visualization to the End Users
P - hacking - Dimensionality Reduction - PCA - PCA using Singular value Decomposition-Decision tree-Random Forest.
Reference Book:
1. Tom M. Mitchell, “Machine Learningâ€, McGraw-Hill Education (India) Private Limited, 2013. 2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning: with Applications in Râ€, Springer; First Edition 2013. 3. P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data, Cambridge University Press, 2012.
Text Book:
1. AlpaydinEthem, “Introduction to Machine Learningâ€, MIT Press, Second Edition, 2010. 2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Predictionâ€, Springer; Second Edition, 2009.