目錄(210章)
倒序
- coverpage
- Title Page
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- A Gentle Introduction to Machine Learning
- Introduction - classic and adaptive machines
- Only learning matters
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Beyond machine learning - deep learning and bio-inspired adaptive systems
- Machine learning and big data
- Further reading
- Summary
- Important Elements in Machine Learning
- Data formats
- Multiclass strategies
- One-vs-all
- One-vs-one
- Learnability
- Underfitting and overfitting
- Error measures
- PAC learning
- Statistical learning approaches
- MAP learning
- Maximum-likelihood learning
- Elements of information theory
- References
- Summary
- Feature Selection and Feature Engineering
- scikit-learn toy datasets
- Creating training and test sets
- Managing categorical data
- Managing missing features
- Data scaling and normalization
- Feature selection and filtering
- Principal component analysis
- Non-negative matrix factorization
- Sparse PCA
- Kernel PCA
- Atom extraction and dictionary learning
- References
- Summary
- Linear Regression
- Linear models
- A bidimensional example
- Linear regression with scikit-learn and higher dimensionality
- Regressor analytic expression
- Ridge Lasso and ElasticNet
- Robust regression with random sample consensus
- Polynomial regression
- Isotonic regression
- References
- Summary
- Logistic Regression
- Linear classification
- Logistic regression
- Implementation and optimizations
- Stochastic gradient descent algorithms
- Finding the optimal hyperparameters through grid search
- Classification metrics
- ROC curve
- Summary
- Naive Bayes
- Bayes' theorem
- Naive Bayes classifiers
- Naive Bayes in scikit-learn
- Bernoulli naive Bayes
- Multinomial naive Bayes
- Gaussian naive Bayes
- References
- Summary
- Support Vector Machines
- Linear support vector machines
- scikit-learn implementation
- Linear classification
- Kernel-based classification
- Radial Basis Function
- Polynomial kernel
- Sigmoid kernel
- Custom kernels
- Non-linear examples
- Controlled support vector machines
- Support vector regression
- References
- Summary
- Decision Trees and Ensemble Learning
- Binary decision trees
- Binary decisions
- Impurity measures
- Gini impurity index
- Cross-entropy impurity index
- Misclassification impurity index
- Feature importance
- Decision tree classification with scikit-learn
- Ensemble learning
- Random forests
- Feature importance in random forests
- AdaBoost
- Gradient tree boosting
- Voting classifier
- References
- Summary
- Clustering Fundamentals
- Clustering basics
- K-means
- Finding the optimal number of clusters
- Optimizing the inertia
- Silhouette score
- Calinski-Harabasz index
- Cluster instability
- DBSCAN
- Spectral clustering
- Evaluation methods based on the ground truth
- Homogeneity
- Completeness
- Adjusted rand index
- References
- Summary
- Hierarchical Clustering
- Hierarchical strategies
- Agglomerative clustering
- Dendrograms
- Agglomerative clustering in scikit-learn
- Connectivity constraints
- References
- Summary
- Introduction to Recommendation Systems
- Naive user-based systems
- User-based system implementation with scikit-learn
- Content-based systems
- Model-free (or memory-based) collaborative filtering
- Model-based collaborative filtering
- Singular Value Decomposition strategy
- Alternating least squares strategy
- Alternating least squares with Apache Spark MLlib
- References
- Summary
- Introduction to Natural Language Processing
- NLTK and built-in corpora
- Corpora examples
- The bag-of-words strategy
- Tokenizing
- Sentence tokenizing
- Word tokenizing
- Stopword removal
- Language detection
- Stemming
- Vectorizing
- Count vectorizing
- N-grams
- Tf-idf vectorizing
- A sample text classifier based on the Reuters corpus
- References
- Summary
- Topic Modeling and Sentiment Analysis in NLP
- Topic modeling
- Latent semantic analysis
- Probabilistic latent semantic analysis
- Latent Dirichlet Allocation
- Sentiment analysis
- VADER sentiment analysis with NLTK
- References
- Summary
- A Brief Introduction to Deep Learning and TensorFlow
- Deep learning at a glance
- Artificial neural networks
- Deep architectures
- Fully connected layers
- Convolutional layers
- Dropout layers
- Recurrent neural networks
- A brief introduction to TensorFlow
- Computing gradients
- Logistic regression
- Classification with a multi-layer perceptron
- Image convolution
- A quick glimpse inside Keras
- References
- Summary
- Creating a Machine Learning Architecture
- Machine learning architectures
- Data collection
- Normalization
- Dimensionality reduction
- Data augmentation
- Data conversion
- Modeling/Grid search/Cross-validation
- Visualization
- scikit-learn tools for machine learning architectures
- Pipelines
- Feature unions
- References
- Summary 更新時間：2021-07-02 18:54:04
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