Python machine learning machine learning and deep learning with Python, scikit-learn, and TensorFlow
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learnin...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai, [India] :
Packt
2017.
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| Edición: | Second edition, fully revised and updated |
| Colección: | Expert Insight
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630672006719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewers
- www.PacktPub.com
- Packt is Searching for Authors Like You
- Table of Contents
- Preface
- Chapter 1: Giving Computers the Ability to Learn from Data
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- Making predictions about the future with supervised learning
- Classification for predicting class labels
- Regression for predicting continuous outcomes
- Solving interactive problems with reinforcement learning
- Discovering hidden structures with unsupervised learning
- Finding subgroups with clustering
- Dimensionality reduction for data compression
- Introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Preprocessing - getting data into shape
- Training and selecting a predictive model
- Evaluating models and predicting unseen data instances
- Using Python for machine learning
- Installing Python and packages from the Python Package Index
- Using the Anaconda Python distribution and package manager
- Packages for scientific computing, data science, and machine learning
- Summary
- Chapter 2: Training Simple Machine Learning Algorithms for Classification
- Artificial neurons - a brief glimpse into the early history of machine learning
- The formal definition of an artificial neuron
- The perceptron learning rule
- Implementing a perceptron learning algorithm in Python
- An object-oriented perceptron API
- Training a perceptron model on the Iris dataset
- Adaptive linear neurons and the convergence of learning
- Minimizing cost functions with gradient descent
- Implementing Adaline in Python
- Improving gradient descent through feature scaling
- Large-scale machine learning and stochastic gradient descent
- Summary.
- Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn - training a perceptron
- Modeling class probabilities via logistic regression
- Logistic regression intuition and conditional probabilities
- Learning the weights of the logistic cost function
- Converting an Adaline implementation into an algorithm for logistic regression
- Training a logistic regression model with scikit-learn
- Tackling overfitting via regularization
- Maximum margin classification with support vector machines
- Maximum margin intuition
- Dealing with a nonlinearly separable case using slack variables
- Alternative implementations in scikit-learn
- Solving nonlinear problems using a kernel SVM
- Kernel methods for linearly inseparable data
- Using the kernel trick to find separating hyperplanes in high-dimensional space
- Decision tree learning
- Maximizing information gain - getting the most bang for your buck
- Building a decision tree
- Combining multiple decision trees via random forests
- K-nearest neighbors - a lazy learning algorithm
- Summary
- Chapter 4: Building Good Training Sets - Data Preprocessing
- Dealing with missing data
- Identifying missing values in tabular data
- Eliminating samples or features with missing values
- Imputing missing values
- Understanding the scikit-learn estimator API
- Handling categorical data
- Nominal and ordinal features
- Creating an example dataset
- Mapping ordinal features
- Encoding class labels
- Performing one-hot encoding on nominal features
- Partitioning a dataset into separate training and test sets
- Bringing features onto the same scale
- Selecting meaningful features
- L1 and L2 regularization as penalties against model complexity
- A geometric interpretation of L2 regularization.
- Sparse solutions with L1 regularization
- Sequential feature selection algorithms
- Assessing feature importance with random forests
- Summary
- Chapter 5: Compressing Data via Dimensionality Reduction
- Unsupervised dimensionality reduction via principal component analysis
- The main steps behind principal component analysis
- Extracting the principal components step by step
- Total and explained variance
- Feature transformation
- Principal component analysis in scikit-learn
- Supervised data compression via linear discriminant analysis
- Principal component analysis versus linear discriminant analysis
- The inner workings of linear discriminant analysis
- Computing the scatter matrices
- Selecting linear discriminants for the new feature subspace
- Projecting samples onto the new feature space
- LDA via scikit-learn
- Using kernel principal component analysis for nonlinear mappings
- Kernel functions and the kernel trick
- Implementing a kernel principal component analysis in Python
- Example 1 - separating half-moon shapes
- Example 2 - separating concentric circles
- Projecting new data points
- Kernel principal component analysis in scikit-learn
- Summary
- Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Streamlining workflows with pipelines
- Loading the Breast Cancer Wisconsin dataset
- Combining transformers and estimators in a pipeline
- Using k-fold cross-validation to assess model performance
- The holdout method
- K-fold cross-validation
- Debugging algorithms with learning and validation curves
- Diagnosing bias and variance problems with learning curves
- Addressing over- and underfitting with validation curves
- Fine-tuning machine learning models via grid search
- Tuning hyperparameters via grid search
- Algorithm selection with nested cross-validation.
- Looking at different performance evaluation metrics
- Reading a confusion matrix
- Optimizing the precision and recall of a classification model
- Plotting a receiver operating characteristic
- Scoring metrics for multiclass classification
- Dealing with class imbalance
- Summary
- Chapter 7: Combining Different Models for Ensemble Learning
- Learning with ensembles
- Combining classifiers via majority vote
- Implementing a simple majority vote classifier
- Using the majority voting principle to make predictions
- Evaluating and tuning the ensemble classifier
- Bagging - building an ensemble of classifiers from bootstrap samples
- Bagging in a nutshell
- Applying bagging to classify samples in the Wine dataset
- Leveraging weak learners via adaptive boosting
- How boosting works
- Applying AdaBoost using scikit-learn
- Summary
- Chapter 8: Applying Machine Learning to Sentiment Analysis
- Preparing the IMDb movie review data for text processing
- Obtaining the movie review dataset
- Preprocessing the movie dataset into more convenient format
- Introducing the bag-of-words model
- Transforming words into feature vectors
- Assessing word relevancy via term frequency-inverse document frequency
- Cleaning text data
- Processing documents into tokens
- Training a logistic regression model for document classification
- Working with bigger data - online algorithms and out-of-core learning
- Topic modeling with Latent Dirichlet Allocation
- Decomposing text documents with LDA
- LDA with scikit-learn
- Summary
- Chapter 9: Embedding a Machine Learning Model into a Web Application
- Serializing fitted scikit-learn estimators
- Setting up an SQLite database for data storage
- Developing a web application with Flask
- Our first Flask web application
- Form validation and rendering
- Setting up the directory structure.
- Implementing a macro using the Jinja2 templating engine
- Adding style via CSS
- Creating the result page
- Turning the movie review classifier into a web application
- Files and folders - looking at the directory tree
- Implementing the main application as app.py
- Setting up the review form
- Creating a results page template
- Deploying the web application to a public server
- Creating a PythonAnywhere account
- Uploading the movie classifier application
- Updating the movie classifier
- Summary
- Chapter 10: Predicting Continuous Target Variables with Regression Analysis
- Introducing linear regression
- Simple linear regression
- Multiple linear regression
- Exploring the Housing dataset
- Loading the Housing dataset into a data frame
- Visualizing the important characteristics of a dataset
- Looking at relationships using a correlation matrix
- Implementing an ordinary least squares linear regression model
- Solving regression for regression parameters with gradient descent
- Estimating coefficient of a regression model via scikit-learn
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve - polynomial regression
- Adding polynomial terms using scikit-learn
- Modeling nonlinear relationships in the Housing dataset
- Dealing with nonlinear relationships using random forests
- Decision tree regression
- Random forest regression
- Summary
- Chapter 11: Working with Unlabeled Data - Clustering Analysis
- Grouping objects by similarity using k-means
- K-means clustering using scikit-learn
- A smarter way of placing the initial cluster centroids using k-means++
- Hard versus soft clustering
- Using the elbow method to find the optimal number of clusters.
- Quantifying the quality of clustering via silhouette plots.
