Python deeper insights into machine learning: leverage benefits of machine learning techniques using Python: a course in three modules

Cover......Page 1
Copyright......Page 3
Credits......Page 4
Preface......Page 6
Table of Contents......Page 10
Giving Computers the Ability to Learn from Data......Page 20
 The three different types of machine learning......Page 21
 An introduction to the basic terminology and notations......Page 27
 A roadmap for building machine learning systems......Page 29
 Using Python for machine learning......Page 32
 Summary......Page 34
Training Machine Learning Algorithms for Classification......Page 36
 Artificial neurons – a brief glimpse into the early history of machine learning......Page 37
 Implementing a perceptron learning algorithm in Python......Page 43
 Adaptive linear neurons and the convergence of learning......Page 52
 Summary......Page 66
 Choosing a classification algorithm......Page 68
 First steps with scikit-learn......Page 69
 Modeling class probabilities via logistic regression......Page 75
 Maximum margin classification with support vector machines......Page 88
 Solving nonlinear problems using a kernel SVM......Page 94
 Decision tree learning......Page 99
 K-nearest neighbors – a lazy learning algorithm......Page 111
 Summary......Page 115
 Dealing with missing data......Page 118
 Handling categorical data......Page 123
 Partitioning a dataset in training and test sets......Page 127
 Bringing features onto the same scale......Page 129
 Selecting meaningful features......Page 131
 Assessing feature importance with random forests......Page 143
 Summary......Page 145
Compressing Data via Dimensionality Reduction......Page 146
 Unsupervised dimensionality reduction via principal component analysis......Page 147
 Supervised data compression via linear discriminant analysis......Page 157
 Using kernel principal component analysis for nonlinear mappings......Page 167
 Summary......Page 186
 Streamlining workflows with pipelines......Page 188
 Using k-fold cross-validation to assess model performance......Page 192
 Debugging algorithms with learning and validation curves......Page 198
 Fine-tuning machine learning models via grid search......Page 204
 Looking at different performance evaluation metrics......Page 208
 Summary......Page 217
 Learning with ensembles......Page 218
 Implementing a simple majority vote classifier......Page 222
 Evaluating and tuning the ensemble classifier......Page 232
 Bagging – building an ensemble of classifiers from bootstrap samples......Page 238
 Leveraging weak learners via adaptive boosting......Page 243
 Summary......Page 251
 Obtaining the IMDb movie review dataset......Page 252
 Introducing the bag-of-words model......Page 255
 Training a logistic regression model for document classification......Page 263
 Working with bigger data – online algorithms and out-of-core learning......Page 265
 Summary......Page 269
Embedding a Machine Learning Model into a Web Application......Page 270
 Serializing fitted scikit-learn estimators......Page 271
 Setting up a SQLite database for data storage......Page 274
 Developing a web application with Flask......Page 276
 Turning the movie classifier into a web application......Page 283
 Deploying the web application to a public server......Page 291
 Summary......Page 295
Predicting Continuous Target Variables with Regression Analysis......Page 296
 Introducing a simple linear regression model......Page 297
 Exploring the Housing Dataset......Page 298
 Implementing an ordinary least squares linear regression model......Page 304
 Fitting a robust regression model using RANSAC......Page 310
 Evaluating the performance of linear regression models......Page 313
 Using regularized methods for regression......Page 316
 Turning a linear regression model into a curve – polynomial regression......Page 317
 Summary......Page 328
Working with Unlabeled Data – Clustering Analysis......Page 330
 Grouping objects by similarity using k-means......Page 331
 Organizing clusters as a hierarchical tree......Page 345
 Locating regions of high density via DBSCAN......Page 353
 Summary......Page 359
Training Artificial Neural Networks for Image Recognition......Page 360
 Modeling complex functions with artificial neural networks......Page 361
 Classifying handwritten digits......Page 369
 Training an artificial neural network......Page 384
 Developing your intuition for backpropagation......Page 391
 Debugging neural networks with gradient checking......Page 392
 Convergence in neural networks......Page 398
 Other neural network architectures......Page 400
 A few last words about neural network implementation......Page 403
 Summary......Page 404
Parallelizing Neural Network Training with Theano......Page 406
 Building, compiling, and running expressions with Theano......Page 407
 Choosing activation functions for feedforward neural networks......Page 420
 Training neural networks efficiently using Keras......Page 427
 Summary......Page 433
Thinking in Machine Learning......Page 438
 The human interface......Page 439
 Design principles......Page 442
 Summary......Page 470
Tools and Techniques......Page 472
 IPython console......Page 473
 Installing the SciPy stack......Page 474
 NumPY......Page 475
 Matplotlib......Page 481
 Pandas......Page 485
 SciPy......Page 488
 Scikit-learn......Page 491
 Summary......Page 498
Turning Data into Information......Page 500
 Big data......Page 501
 Signals......Page 517
 Cleaning data......Page 519
 Visualizing data......Page 521
 Summary......Page 524
 Logical models......Page 526
 Tree models......Page 534
 Rule models......Page 538
 Summary......Page 545
Linear Models......Page 546
 Introducing least squares......Page 547
 Logistic regression......Page 555
 Multiclass classification......Page 561
 Regularization......Page 562
 Summary......Page 565
 Getting started with neural networks......Page 566
 Logistic units......Page 568
 Cost function......Page 573
 Implementing a neural network......Page 576
 Gradient checking......Page 582
 Other neural net architectures......Page 583
 Summary......Page 584
Features – How Algorithms See the World......Page 586
 Feature types......Page 587
 Operations and statistics......Page 588
 Transforming features......Page 591
 Principle component analysis......Page 600
 Summary......Page 602
 Ensemble types......Page 604
 Bagging......Page 605
 Boosting......Page 611
 Ensemble strategies......Page 618
 Summary......Page 621
 Evaluating model performance......Page 622
 Model selection......Page 627
 Learning curves......Page 630
 Real-world case studies......Page 632
 Machine learning at a glance......Page 643
 Summary......Page 644
Unsupervised Machine Learning......Page 648
 Principal component analysis......Page 649
 Introducing k-means clustering......Page 654
 Self-organizing maps......Page 665
 Further reading......Page 671
 Summary......Page 672
Deep Belief Networks......Page 674
 Neural networks – a primer......Page 675
 Restricted Boltzmann Machine......Page 680
 Deep belief networks......Page 696
 Further reading......Page 702
 Summary......Page 703
 Autoencoders......Page 704
 Stacked Denoising Autoencoders......Page 713
 Summary......Page 722
 Introducing the CNN......Page 724
 Further Reading......Page 746
 Summary......Page 747
 Introduction......Page 748
 Understanding semi-supervised learning......Page 749
 Semi-supervised algorithms in action......Page 750
 Further reading......Page 773
 Summary......Page 774
 Introduction......Page 776
 Text feature engineering......Page 777
 Further reading......Page 800
 Summary......Page 801
 Introduction......Page 802
 Creating a feature set......Page 803
 Feature engineering in practice......Page 822
 Further reading......Page 846
 Summary......Page 847
Ensemble Methods......Page 848
 Introducing ensembles......Page 849
 Using models in dynamic applications......Page 868
 Further reading......Page 880
 Summary......Page 881
Additional Python Machine Learning Tools......Page 882
 Alternative development tools......Page 883
 Summary......Page 892
Chapter Code Requirements......Page 896
Biblography......Page 898