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Machine Learning with scikit：learn Quick Start Guide
Scikit-learnisarobustmachinelearninglibraryforthePythonprogramminglanguage.Itprovidesasetofsupervisedandunsupervisedlearningalgorithms.Thisbookistheeasiestwaytolearnhowtodeploy,optimize,andevaluatealloftheimportantmachinelearningalgorithmsthatscikit-learnprovides.Thisbookteachesyouhowtousescikit-learnformachinelearning.Youwillstartbysettingupandconfiguringyourmachinelearningenvironmentwithscikit-learn.Toputscikit-learntouse,youwilllearnhowtoimplementvarioussupervisedandunsupervisedmachinelearningmodels.Youwilllearnclassification,regression,andclusteringtechniquestoworkwithdifferenttypesofdatasetsandtrainyourmodels.Finally,youwilllearnaboutaneffectivepipelinetohelpyoubuildamachinelearningprojectfromscratch.Bytheendofthisbook,youwillbeconfidentinbuildingyourownmachinelearningmodelsforaccuratepredictions.
目錄(152章)
倒序
- coverpage
- Title Page
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Code in action
- Conventions used
- Get in touch
- Reviews
- 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
- Predicting Categories with K-Nearest Neighbors
- Technical 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
- Predicting Categories with Logistic Regression
- Technical requirements
- Understanding 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
- 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 optimization
- Hyperparameter optimization using GridSearchCV
- Scaling the data for performance improvement
- Summary
- 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
- Classification and Regression with Trees
- Technical requirements
- Classification trees
- The 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
- Implementing the decision tree regressor in scikit-learn
- Visualizing the decision tree regressor
- The random forest regressor
- Implementing the random forest regressor in scikit-learn
- The gradient boosted tree
- Implementing the gradient boosted tree in scikit-learn
- Ensemble classifier
- Implementing the voting classifier in scikit-learn
- Summary
- Clustering Data with Unsupervised Machine Learning
- Technical requirements
- The k-means algorithm
- Assignment of centroids
- When does the algorithm stop iterating?
- Implementing the k-means algorithm in scikit-learn
- Creating the base k-means model
- The optimal number of clusters
- Feature engineering for optimization
- Scaling
- Principal component analysis
- Cluster visualization
- t-SNE
- Hierarchical clustering
- Step 1 – Individual features as individual clusters
- Step 2 – The merge
- Step 3 – Iteration
- Implementing hierarchical clustering
- Going from unsupervised to supervised learning
- Creating a labeled dataset
- Building the decision tree
- Summary
- Performance Evaluation Methods
- Technical requirements
- Why is performance evaluation critical?
- Performance evaluation for classification algorithms
- The confusion matrix
- The normalized confusion matrix
- Area under the curve
- Cumulative gains curve
- Lift curve
- K-S statistic plot
- Calibration plot
- Learning curve
- Cross-validated box plot
- Performance evaluation for regression algorithms
- Mean absolute error
- Mean squared error
- Root mean squared error
- Performance evaluation for unsupervised algorithms
- Elbow plot
- Summary
- Other Books You May Enjoy
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