Introduction to Machine Learning with Python: A Guide for Data Scientists

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"O'Reilly Media, Inc.", 26.09.2016 - 400 Seiten

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills
 

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Urheberrecht

Andere Ausgaben - Alle anzeigen

Introduction to Machine Learning with Python: A Guide for Data Scientists
Andreas C. Müller,Sarah Guido
Eingeschränkte Leseprobe - 2016
Introduction to Machine Learning with Python: A Guide for Data Scientists
Andreas C. Muller,Sarah Guido
Keine Leseprobe verfügbar - 2018
Introduction to Machine Learning with Python: A Guide for Data Scientists
Andreas C. Müller,Sarah Guido
Keine Leseprobe verfügbar - 2016

Häufige Begriffe und Wortgruppen

agglomerative clustering apply axes bag-of-words binary classification build categorical Chapter cluster centers coefficients complex compute cross-validation score data points DBSCAN decision boundary decision trees decision_function default encoding example f-score feature extraction feature selection Figure function gamma gradient boosting grid search GridSearchCV images input k-means k-nearest neighbors kernel labels Lemmatization linear models linear regression LogisticRegression look machine learning machine learning algorithms machine learning model matplotlib matrix means method metrics min_samples MinMaxScaler models for classification multiclass classification Naive Bayes Classifiers neural networks number of clusters number of features NumPy output overfitting param_grid performance pipeline plot polynomial predictions preprocessing principal components print("Accuracy print("Test set Python random forest random_state=0 representation Sample Datasets scaling scikit-learn SciPy split StandardScaler supervised learning support vector support vector machines t-SNE test data tf–idf topic train_test_split training and test training data training set transform True True Univariate unsupervised values visualize words y_test y_train

Autoren-Profil (2016)

Andreas Müller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.

Bibliografische Informationen

TitelIntroduction to Machine Learning with Python: A Guide for Data Scientists
AutorenAndreas C. Müller, Sarah Guido
Verlag"O'Reilly Media, Inc.", 2016
ISBN1449369898, 9781449369897
Länge400 Seiten
  
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