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

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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