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Mastering Machine Learning with scikit-learn.
If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2014.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Fundamentals of Machine Learning; Learning from experience; Machine learning tasks; Training data and test data; Performance measures, bias, and variance; An introduction to scikit-learn; Installing scikit-learn; Installing scikit-learn on Windows; Installing scikit-learn on Linux; Installing scikit-learn on OS X; Verifying the installation; Installing pandas and matplotlib; Summary; Chapter 2: Linear Regression; Simple linear regression.
- Evaluating the fitness of a model with a cost functionSolving ordinary least squares for simple linear regression; Evaluating the model; Multiple linear regression; Polynomial regression; Regularization; Applying linear regression; Exploring the data; Fitting and evaluating the model; Fitting models with gradient descent; Summary; Chapter 3: Feature Extraction and Pre-Processing; Extracting features from categorical variables; Extracting features from text; The bag-of-words representation; Stop-word filtering; Stemming and lemmatization; Extending bag-of-words with tf-idf weights.
- Space-efficient feature vectorizing with the hashing trickExtracting features from images; Extracting features from pixel intensities; Extracting points of interest as features; SIFT and SURF; Data standardization; Summary; Chapter 4: From Linear Regression to Logistic Regression; Binary classification with logistic regression; Spam filtering; Binary classification performance metrics; Accuracy; Precision and recall; Calculating the F1 measure; ROC AUC; Tuning models with grid search; Multi-class classification; Multi-class classification performance metrics.
- Multi-label classification and problem transformationMulti-label classification performance metrics; Summary; Chapter 5: Non-linear Classification and Regression with Decision Trees; Decision trees; Training decision trees; Selecting the questions; Information gain; Gini impurity; Decision trees with scikit-learn; Tree ensembles; The advantages and disadvantages of decision trees; Summary; Chapter 6: Clustering with K-Means; Clustering with the K-Means algorithm; Local optima; The elbow method; Evaluating clusters; Image quantization; Clustering to learn features; Summary.
- Chapter 7: Dimensionality Reduction with PCAAn overview of PCA; Performing Principal Component Analysis; Variance, Covariance, and Covariance Matrices; Eigenvectors and eigenvalues; Dimensionality reduction with Principal Component Analysis; Using PCA to visualize high-dimensional data; Face recognition with PCA; Summary; Chapter 8: The Perceptron; Activation functions; The perceptron learning algorithm; Binary classification with the perceptron; Document classification with the perceptron; Limitations of the perceptron; Summary; Chapter 9: From the Perceptron to Support Vector Machines.