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Preface
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Chapter 8, Machine Learning, covers methods to learn and make predictions from data.
Using the scikit-learn Python package, this chapter illustrates fundamental data mining and
machine learning concepts such as supervised and unsupervised learning, classication,
regression, feature selection, feature extraction, overtting, regularization, cross-validation,
and grid search. Algorithms addressed in this chapter include logistic regression, Naive Bayes,
K-nearest neighbors, Support Vector Machines, random forests, and others. These methods
are applied to various types of datasets: numerical data, images, and text.
Chapter 9, Numerical Optimization, is about minimizing or maximizing mathematical
functions. This topic is pervasive in data science, notably in statistics, machine learning, and
signal processing. This chapter illustrates a few root-nding, minimization, and curve tting
routines with SciPy.
Chapter 10, Signal Processing, is about extracting relevant information from complex and
noisy data. These steps are sometimes required prior to running statistical and data mining
algorithms. This chapter introduces standard signal processing methods such as Fourier
transforms and digital lters.
Chapter 11, Image and Audio Processing, covers signal processing methods for images and
sounds. It introduces image ltering, segmentation, computer vision, and face detection with
scikit-image and OpenCV. It also presents methods for audio processing and synthesis.
Chapter 12, Deterministic Dynamical Systems, describes dynamical processes underlying
particular types of data. It illustrates simulation techniques for discrete-time dynamical
systems as well as for ordinary differential equations and partial differential equations.
Chapter 13, Stochastic Dynamical Systems, describes dynamical random processes
underlying particular types of data. It illustrates simulation techniques for discrete-time
Markov chains, point processes, and stochastic differential equations.
Chapter 14, Graphs, Geometry, and Geographic Information Systems, covers analysis and
visualization methods for graphs, social networks, road networks, maps, and geographic data.
Chapter 15, Symbolic and Numerical Mathematics, introduces SymPy, a computer algebra
system that brings symbolic computing to Python. The chapter ends with an introduction to
Sage, another Python-based system for computational mathematics.
What you need for this book
You need to know the content of this book's prequel, Learning IPython for Interactive
Computing and Data Visualization: Python programming, the IPython console and notebook,
numerical computing with NumPy, basic data analysis with pandas as well as plotting with
matplotlib. This book tackles advanced scientic programming topics that require you to be
familiar with the scientic Python ecosystem.
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