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Machine Learning with Scikit-Learn Quick Start Guide : Classification, Regression, and Clustering Techniques in Python.
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize and evaluate all the important machine learning algorithms that scikit-learn prov...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introducing Machine Learning with scikit-learn; A brief introduction to machine learning; Supervised learning; Unsupervised learning; What is scikit-learn?; Installing scikit-learn; The pip method; The Anaconda method; Additional packages; Pandas; Matplotlib; Tree; Pydotplus; Image; Algorithms that you will learn to implement using scikit-learn; Supervised learning algorithms; Unsupervised learning algorithms; Summary.
- Chapter 2: Predicting Categories with K-Nearest NeighborsTechnical requirements; Preparing a dataset for machine learning with scikit-learn; Dropping features that are redundant; Reducing the size of the data; Encoding the categorical variables; Missing values; The k-NN algorithm; Implementing the k-NN algorithm using scikit-learn; Splitting the data into training and test sets; Implementation and evaluation of your model; Fine-tuning the parameters of the k-NN algorithm; Scaling for optimized performance; Summary; Chapter 3: Predicting Categories with Logistic Regression.
- Technical requirementsUnderstanding logistic regression mathematically ; Implementing logistic regression using scikit-learn; Splitting the data into training and test sets; Fine-tuning the hyperparameters; Scaling the data; Interpreting the logistic regression model; Summary; Chapter 4: Predicting Categories with Naive Bayes and SVMs; Technical requirements; The Naive Bayes algorithm ; Implementing the Naive Bayes algorithm in scikit-learn; Support vector machines; Implementing the linear support vector machine algorithm in scikit-learn; Hyperparameter optimization for the linear SVMs.
- Graphical hyperparameter optimizationHyperparameter optimization using GridSearchCV; Scaling the data for performance improvement; Summary; Chapter 5: Predicting Numeric Outcomes with Linear Regression; Technical requirements; The inner mechanics of the linear regression algorithm; Implementing linear regression in scikit-learn; Linear regression in two dimensions ; Using linear regression to predict mobile transaction amount; Scaling your data; Model optimization ; Ridge regression; Lasso regression; Summary; Chapter 6: Classification and Regression with Trees; Technical requirements.
- Classification treesThe decision tree classifier; Picking the best feature; The Gini coefficient; Implementing the decision tree classifier in scikit-learn; Hyperparameter tuning for the decision tree; Visualizing the decision tree; The random forests classifier; Implementing the random forest classifier in scikit-learn; Hyperparameter tuning for random forest algorithms; The AdaBoost classifier; Implementing the AdaBoost classifier in scikit-learn; Hyperparameter tuning for the AdaBoost classifier; Regression trees; The decision tree regressor.