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Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Python is one of the top 3 tools that Data Scientists use.One of the tools in their arsenal is the Pandas library.This tool is popular because it gives you so much functionality out of the box.In addition, you can use all the power of Python to make the hard stuff easy! Learning the Pandas Libraryis designed to bring developers and aspiring data scientists who are anxious to learn Pandas up to speed quickly. It starts with the fundamentals of the data structures. Then, it covers the essential functionality. It includes many examples, graphics, code samples, and plots from real world examples. The Content Covers: Installation Data Structures Series CRUD Series Indexing Series Methods Series Plotting Series Examples DataFrame Methods DataFrame Statistics Grouping, Pivoting, and Reshaping Dealing with Missing Data Joining DataFrames DataFrame ExamplesPreliminary ReviewsThis is anexcellent introduction benefitting from clear writing and simple examples. The pandas documentation itself is large and sometimes assumes too much knowledge, in my opinion. Learning the Pandas Library bridges this gap for new users and even for those with some pandas experience such as me. -Garry C. I have finished readingLearning the Pandas Libraryand I liked it... very useful and helpful tips even for people who use pandas regularly. -Tom Z.