Python: Deeper Insights into Machine Learning. Deeper Insights into Machine Learning

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• Python: Deeper Insights into Machine Learning
  • Table of Contents
  • Python: Deeper Insights into Machine Learning
  • Python: Deeper Insights into Machine Learning
  • Credits
  • Preface
    • What this learning path covers
    • What you need for this learning path
    • Who this learning path is for
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 1. Module 1
    • 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
      • An 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 packages
      • Summary
    • 2. Training Machine Learning Algorithms for Classification
      • Artificial neurons a brief glimpse into the early history of machine learning
      • Implementing a perceptron learning algorithm in Python
        • Training a perceptron model on the Iris dataset
      • Adaptive linear neurons and the convergence of learning
        • Minimizing cost functions with gradient descent
        • Implementing an Adaptive Linear Neuron in Python
        • Large scale machine learning and stochastic gradient descent
      • Summary
    • 3. A Tour of Machine Learning Classifiers Using Scikit-learn
      • Choosing a classification algorithm
      • First steps with scikit-learn
        • Training a perceptron via scikit-learn
      • Modeling class probabilities via logistic regression
        • Logistic regression intuition and conditional probabilities
        • Learning the weights of the logistic cost function
        • Training a logistic regression model with scikit-learn
        • Tackling overfitting via regularization
      • Maximum margin classification with support vector machines
        • Maximum margin intuition
        • Dealing with the nonlinearly separable case using slack variables
        • Alternative implementations in scikit-learn
      • Solving nonlinear problems using a kernel SVM
        • Using the kernel trick to find separating hyperplanes in higher dimensional space
      • Decision tree learning
        • Maximizing information gain getting the most bang for the buck
        • Building a decision tree
        • Combining weak to strong learners via random forests
      • K-nearest neighbors a lazy learning algorithm
      • Summary
    • 4. Building Good Training Sets Data Preprocessing
      • Dealing with missing data
        • Eliminating samples or features with missing values
        • Imputing missing values
        • Understanding the scikit-learn estimator API
      • Handling categorical data
        • Mapping ordinal features
        • Encoding class labels
        • Performing one-hot encoding on nominal features
      • Partitioning a dataset in training and test sets
      • Bringing features onto the same scale
      • Selecting meaningful features
        • Sparse solutions with L1 regularization
        • Sequential feature selection algorithms
      • Assessing feature importance with random forests
      • Summary
    • 5. Compressing Data via Dimensionality Reduction
      • Unsupervised dimensionality reduction via principal component analysis
        • Total and explained variance
        • Feature transformation
        • Principal component analysis in scikit-learn
      • Supervised data compression via 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
    • 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 overfitting 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
        • The scoring metrics for multiclass classification
      • Summary
    • 7. Combining Different Models for Ensemble Learning
      • Learning with ensembles
      • Implementing a simple majority vote classifier
        • Combining different algorithms for classification with majority vote
      • Evaluating and tuning the ensemble classifier
      • Bagging building an ensemble of classifiers from bootstrap samples
      • Leveraging weak learners via adaptive boosting
      • Summary
    • 8. Applying Machine Learning to Sentiment Analysis
      • Obtaining the IMDb movie review dataset
      • 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
      • Summary
    • 9. Embedding a Machine Learning Model into a Web Application
      • Serializing fitted scikit-learn estimators
      • Setting up a SQLite database for data storage
      • Developing a web application with Flask
        • Our first Flask web application
        • Form validation and rendering
      • Turning the movie classifier into a web application
      • Deploying the web application to a public server
        • Updating the movie review classifier
      • Summary
    • 10. Predicting Continuous Target Variables with Regression Analysis
      • Introducing a simple linear regression model
      • Exploring the Housing Dataset
        • Visualizing the important characteristics of a dataset
      • Implementing an ordinary least squares linear regression model
        • Solving regression for regression parameters with gradient descent
        • Estimating the 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
        • Modeling nonlinear relationships in the Housing Dataset
        • Dealing with nonlinear relationships using random forests
          • Decision tree regression
          • Random forest regression
      • Summary
    • 11. Working with Unlabeled Data Clustering Analysis
      • Grouping objects by similarity using k-means
        • 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
      • Organizing clusters as a hierarchical tree
        • Performing hierarchical clustering on a distance matrix
        • Attaching dendrograms to a heat map
        • Applying agglomerative clustering via scikit-learn
      • Locating regions of high density via DBSCAN
      • Summary
    • 12. Training Artificial Neural Networks for Image Recognition
      • Modeling complex functions with artificial neural networks
        • Single-layer neural network recap
        • Introducing the multi-layer neural network architecture
        • Activating a neural network via forward propagation
      • Classifying handwritten digits
        • Obtaining the MNIST dataset
        • Implementing a multi-layer perceptron
      • Training an artificial neural network
        • Computing the logistic cost function
        • Training neural networks via backpropagation
      • Developing your intuition for backpropagation
      • Debugging neural networks with gradient checking
      • Convergence in neural networks
      • Other neural network architectures
        • Convolutional Neural Networks
        • Recurrent Neural Networks
      • A few last words about neural network implementation
      • Summary
    • 13. Parallelizing Neural Network Training with Theano
      • Building, compiling, and running expressions with Theano
        • What is Theano?
        • First steps with Theano
        • Configuring Theano
        • Working with array structures
        • Wrapping things up a linear regression example
      • Choosing activation functions for feedforward neural networks
        • Logistic function recap
        • Estimating probabilities in multi-class classification via the softmax function
        • Broadening the output spectrum by using a hyperbolic tangent
      • Training neural networks efficiently using Keras
      • Summary
  • 2. Module 2
    • 1. Thinking in Machine Learning
      • The human interface
      • Design principles
        • Types of questions
        • Are you asking the right question?
        • Tasks
          • Classification
          • Regression
          • Clustering
          • Dimensionality reduction
          • Errors
          • Optimization
          • Linear programming
          • Models
            • Geometric models
            • Probabilistic models
            • Logical models
          • Features
        • Unified modeling language
          • Class diagrams
          • Object diagrams
          • Activity diagrams
          • State diagrams
      • Summary
    • 2. Tools and Techniques
      • Python for machine learning
      • IPython console
      • Installing the SciPy stack
      • NumPY
        • Constructing and transforming arrays
        • Mathematical operations
      • Matplotlib
      • Pandas
      • SciPy
      • Scikit-learn
      • Summary
    • 3. Turning Data into Information
      • What is data?
      • Big data
        • Challenges of big data
          • Data volume
          • Data velocity
          • Data variety
        • Data models
        • Data distributions
        • Data from databases
        • Data from the Web
        • Data from natural language
        • Data from images
        • Data from application programming interfaces
      • Signals
        • Data from sound
      • Cleaning data
      • Visualizing data
      • Summary
    • 4. Models Learning from Information
      • Logical models
        • Generality ordering
        • Version space
        • Coverage space
        • PAC learning and computational complexity
      • Tree models
        • Purity
      • Rule models
        • The ordered list approach
        • Set-based rule models
      • Summary
    • 5. Linear Models
      • Introducing least squares
        • Gradient descent
        • The normal equation
      • Logistic regression
        • The Cost function for logistic regression
      • Multiclass classification
      • Regularization
      • Summary
    • 6. Neural Networks
      • Getting started with neural networks
      • Logistic units
      • Cost function
        • Minimizing the cost function
      • Implementing a neural network
      • Gradient checking
      • Other neural net architectures
      • Summary
    • 7. Features How Algorithms See the World
      • Feature types
        • Quantitative features
        • Ordinal features
        • Categorical features
      • Operations and statistics
      • Structured features
      • Transforming features
        • Discretization
        • Normalization
        • Calibration
      • Principle component analysis
      • Summary
    • 8. Learning with Ensembles
      • Ensemble types
      • Bagging
        • Random forests
        • Extra trees
      • Boosting
        • Adaboost
        • Gradient boosting
      • Ensemble strategies
        • Other methods
      • Summary
    • 9. Design Strategies and Case Studies
      • Evaluating model performance
      • Model selection
        • Gridsearch
      • Learning curves
      • Real-world case studies
        • Building a recommender system
          • Content-based filtering
          • Collaborative filtering
          • Reviewing the case study
        • Insect detection in greenhouses
          • Reviewing the case study
      • Machine learning at a glance
      • Summary
  • 3. Module 3
    • 1. Unsupervised Machine Learning
      • Principal component analysis
        • PCA a primer
        • Employing PCA
      • Introducing k-means clustering
        • Clustering a primer
        • Kick-starting clustering analysis
        • Tuning your clustering configurations
      • Self-organizing maps
        • SOM a primer
        • Employing SOM
      • Further reading
      • Summary
    • 2. Deep Belief Networks
      • Neural networks a primer
        • The composition of a neural network
        • Network topologies
      • Restricted Boltzmann Machine
        • Introducing the RBM
          • Topology
          • Training
        • Applications of the RBM
        • Further applications of the RBM
      • Deep belief networks
        • Training a DBN
        • Applying the DBN
        • Validating the DBN
      • Further reading
      • Summary
    • 3. Stacked Denoising Autoencoders
      • Autoencoders
        • Introducing the autoencoder
          • Topology
          • Training
        • Denoising autoencoders
        • Applying a dA
      • Stacked Denoising Autoencoders
        • Applying the SdA
        • Assessing SdA performance
      • Further reading
      • Summary
    • 4. Convolutional Neural Networks
      • Introducing the CNN
        • Understanding the convnet topology
          • Understanding convolution layers
          • Understanding pooling layers
          • Training a convnet
          • Putting it all together
        • Applying a CNN
      • Further Reading
      • Summary
    • 5. Semi-Supervised Learning
      • Introduction
      • Understanding semi-supervised learning
      • Semi-supervised algorithms in action
        • Self-training
          • Implementing self-training
          • Finessing your self-training implementation
            • Improving the selection process
        • Contrastive Pessimistic Likelihood Estimation
      • Further reading
      • Summary
    • 6. Text Feature Engineering
      • Introduction
      • Text feature engineering
        • Cleaning text data
          • Text cleaning with BeautifulSoup
          • Managing punctuation and tokenizing
          • Tagging and categorising words
            • Tagging with NLTK
            • Sequential tagging
            • Backoff tagging
        • Creating features from text data
          • Stemming
          • Bagging and random forests
        • Testing our prepared data
      • Further reading
      • Summary
    • 7. Feature Engineering Part II
      • Introduction
      • Creating a feature set
        • Engineering features for ML applications
          • Using rescaling techniques to improve the learnability of features
          • Creating effective derived variables
          • Reinterpreting non-numeric features
        • Using feature selection techniques
          • Performing feature selection
            • Correlation
            • LASSO
            • Recursive Feature Elimination
            • Genetic models
      • Feature engineering in practice
        • Acquiring data via RESTful APIs
          • Testing the performance of our model
          • Twitter
            • Translink Twitter
            • Consumer comments
            • The Bing Traffic API
          • Deriving and selecting variables using feature engineering techniques
            • The weather API
      • Further reading
      • Summary
    • 8. Ensemble Methods
      • Introducing ensembles
        • Understanding averaging ensembles
          • Using bagging algorithms
          • Using random forests
        • Applying boosting methods
          • Using XGBoost
        • Using stacking ensembles
          • Applying ensembles in practice
      • Using models in dynamic applications
        • Understanding model robustness
          • Identifying modeling risk factors
        • Strategies to managing model robustness
      • Further reading
      • Summary
    • 9. Additional Python Machine Learning Tools
      • Alternative development tools
        • Introduction to Lasagne
          • Getting to know Lasagne
        • Introduction to TensorFlow
          • Getting to know TensorFlow
          • Using TensorFlow to iteratively improve our models
        • Knowing when to use these libraries
      • Further reading
      • Summary
    • 10. Chapter Code Requirements
  • A. Biblography
  • Index

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