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Chapter 1 The Two Essential Algorithms for Making Predictions Chapter 2 Understand the Problem by Understanding the Data Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data Chapter 4 Penalized Linear Regression Chapter 5 Building Predictive Models Using Penalized Linear Methods Chapter 6 Ensemble Methods Chapter 7 Building Ensemble Models with Python

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Get more from your data through creating practical machine learning systems with Python

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Get a crash course in Python ■■ Learn the basics of linear algebra, statistics, and probability— and understand how and when they're used in data science ■■ Collect, explore, clean, munge, and manipulate data ■■ Dive into the fundamentals of machine learning ■■ Implement models such as k-nearest neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering ■■ Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

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Master how to build dynamic HTML5-ready SVG charts using Python and the pygal library

Learning SciPy for Numerical and Scientific Computing(2ed,2015)

Quick solutions to complex numerical problems in physics, applied mathematics, and science with SciPy