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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
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Learning Data Mining with Python

Learning Data Mining with Python

By: Robert Layton

Overview of this book

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Table of Contents (14 chapters)
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An Introduction to TensorFlow

TensorFlow is a graph computation library designed by engineers at Google, and is starting to power many of Google's recent advances in deep learning and artificial intelligence.

A graph computation library has two steps. They are listed below:

  1. Defining the sequence (or more complex graphs) of operations that take the input data, operate on it, and convert to outputs.
  2. Compute on the graph obtained from step 1 with a given input.

Many programmers don't use this type of programming day-to-day, but most of them interact with a related system that does. Relational databases, specifically SQL-based ones, use a similar concept called the declarative paradigm. While a programmer might define a SELECT query on a database with a WHERE clause, the database interprets that and creates an optimised query based on a number of factors, such as whether the WHERE clause is applied to a...

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