Книга Machine Learning For Dummies

Несколько дней назад авторами бесплатно раздавалась книга посвященная основам Машинного Обучения “Machine Learning For Dummies” Мы ее бережно для вас сохранили и предлагаем вам с ней ознакомиться. Внимание! Книга на английском языке. Однако, если вы владеете им хотя бы на уровне чтения технической литературы, то проблем у вас не возникнет. А если не владеете… Зачем тогда вам всё это? 🙂

Для начала, что бы было понятно о чём речь, оглавление:

Машинное обучение для чайников
Машинное обучение для чайников


About This Book. 1
Foolish Assumptions. 2
Icons Used in This Book. 3
Beyond the Book. 4
Where to Go from Here. 5


CHAPTER 1: Getting the Real Story about AI

Moving beyond the Hype. 10
Dreaming of Electric Sheep. 11
Understanding the history of AI and machine learning. 12
Exploring what machine learning can do for AI . 13
Considering the goals of machine learning. 13
Defining machine learning limits based on hardware. 14
Overcoming AI Fantasies. 15
Discovering the fad uses of AI and machine learning. 16
Considering the true uses of AI and machine learning. 16
Being useful; being mundane. 18
Considering the Relationship between AI and Machine Learning. 19
Considering AI and Machine Learning Specifications . 20
Defining the Divide between Art and Engineering. 20

CHAPTER 2: Learning in the Age of Big Data

Defining Big Data. 24
Considering the Sources of Big Data . 25
Building a new data source. 26
Using existing data sources. 27
Locating test data sources. 28
Specifying the Role of Statistics in Machine Learning . 29
Understanding the Role of Algorithms. 30
Defining what algorithms do. 30
Considering the five main techniques. 30
Defining What Training Means . 32

CHAPTER 3: Having a Glance at the Future

Creating Useful Technologies for the Future . 36
Considering the role of machine learning in robots. 36
Using machine learning in health care. 37
Creating smart systems for various needs . 37

Using machine learning in industrial settings. 38
Understanding the role of updated processors
and other hardware . 39
Discovering the New Work Opportunities with
Machine Learning. 39
Working for a machine. 40
Working with machines . 41
Repairing machines. 41
Creating new machine learning tasks..42
Devising new machine learning environments.
Avoiding the Potential Pitfalls of Future Technologies . 43


CHAPTER 4: Installing an R Distribution

Choosing an R Distribution with Machine Learning in Mind. 48
Installing R on Windows. 49
Installing R on Linux . 56
Installing R on Mac OS X. 57
Downloading the Datasets and Example Code. 59
Understanding the datasets used in this book. 59
Defining the code repository. 60

CHAPTER 5: Coding in R Using RStudio

Understanding the Basic Data Types. 64
Working with Vectors. 66
Organizing Data Using Lists. 66
Working with Matrices . 67
Creating a basic matrix. 68
Changing the vector arrangement. 69
Accessing individual elements. 69
Naming the rows and columns. 70
Interacting with Multiple Dimensions Using Arrays. 71
Creating a basic array. 71
Naming the rows and columns. 72
Creating a Data Frame. 74
Understanding factors. 74
Creating a basic data frame. 76
Interacting with data frames. 77
Expanding a data frame. 79
Performing Basic Statistical Tasks. 80
Making decisions. 80
Working with loops. 82
Performing looped tasks without loops. 84
Working with functions. 85
Finding mean and median. 85
Charting your data .87

CHAPTER 6: Installing a Python Distribution

Choosing a Python Distribution with Machine Learning in Mind. 90
Getting Continuum Analytics Anaconda . 91
Getting Enthought Canopy Express. 92
Getting pythonxy. 93
Getting WinPython . 93
Installing Python on Linux. 93
Installing Python on Mac OS X. 94
Installing Python on Windows. 96
Downloading the Datasets and Example Code. 99
Using Jupyter Notebook. 100
Defining the code repository. 101
Understanding the datasets used in this book. 106

CHAPTER 7: Coding in Python Using Anaconda

Working with Numbers and Logic. 110
Performing variable assignments. 112
Doing arithmetic . 113
Comparing data using Boolean expressions. 115
Creating and Using Strings. 117
Interacting with Dates. 118
Creating and Using Functions. 119
Creating reusable functions. 119
Calling functions . 121
Working with global and local variables. 123
Using Conditional and Loop Statements. 124
Making decisions using the if statement. 124
Choosing between multiple options using nested decisions. 125
Performing repetitive tasks using for. 126
Using the while statement. 127
Storing Data Using Sets, Lists, and Tuples. 128
Creating sets. 128
Performing operations on sets. 128
Creating lists. 129
Creating and using tuples . 131
Defining Useful Iterators . 132
Indexing Data Using Dictionaries . 134
Storing Code in Modules . 134

CHAPTER 8: Exploring Other Machine Learning Tools

Meeting the Precursors SAS, Stata, and SPSS. 138
Learning in Academia with Weka . 140
Accessing Complex Algorithms Easily Using LIBSVM. 141
Running As Fast As Light with Vowpal Wabbit . 142
Visualizing with Knime and RapidMiner. 143
Dealing with Massive Data by Using Spark. 144


CHAPTER 9: Demystifying the Math Behind

Machine Learning. 147
Working with Data. 148
Creating a matrix. 150
Understanding basic operations. 152
Performing matrix multiplication. 152
Glancing at advanced matrix operations. 155
Using vectorization effectively. 155
Exploring the World of Probabilities. 158
Operating on probabilities. 159
Conditioning chance by Bayes’ theorem. 160
Describing the Use of Statistics. 163

CHAPTER 10: Descending the Right Curve

Interpreting Learning As Optimization. 168
Supervised learning. 168
Unsupervised learning. 169
Reinforcement learning. 169
The learning process. 170
Exploring Cost Functions. 173
Descending the Error Curve. 174
Updating by Mini-Batch and Online. 177

CHAPTER 11: Validating Machine Learning

Checking Out-of-Sample Errors. 182
Looking for generalization. 183
Getting to Know the Limits of Bias. 184
Keeping Model Complexity in Mind. 186
Keeping Solutions Balanced.188
Depicting learning curves. 189
Training, Validating, and Testing. 191
Resorting to Cross-Validation . 191
Looking for Alternatives in Validation.193
Optimizing Cross-Validation Choices. 194
Exploring the space of hyper-parameters. 195
Avoiding Sample Bias and Leakage Traps. 196
Watching out for snooping. 198

CHAPTER 12: Starting with Simple Learners

Discovering the Incredible Perceptron. 200
Falling short of a miracle . 200
Touching the nonseparability limit. 202
Growing Greedy Classification Trees. 204
Predicting outcomes by splitting data . 204
Pruning overgrown trees. 208
Taking a Probabilistic Turn. 209
Understanding Naïve Bayes. 209
Estimating response with Naïve Bayes. 212


CHAPTER 13: Preprocessing Data

Gathering and Cleaning Data . 220
Repairing Missing Data. 221
Identifying missing data. 221
Choosing the right replacement strategy . 222
Transforming Distributions. 225
Creating Your Own Features. 227
Understanding the need to create features . 227
Creating features automatically . 228
Compressing Data. 230
Delimiting Anomalous Data. 232

CHAPTER 14: Leveraging Similarity

Measuring Similarity between Vectors. 238
Understanding similarity . 238
Computing distances for learning. 239
Using Distances to Locate Clusters. 240
Checking assumptions and expectations. 241
Inspecting the gears of the algorithm . 243
Tuning the K-Means Algorithm. 244
Experimenting K-means reliability . 245
Experimenting with how centroids converge. 247
Searching for Classification by K-Nearest Neighbors. 251
Leveraging the Correct K Parameter . 252
Understanding the k parameter. 252
Experimenting with a flexible algorithm . 253

CHAPTER 15: Working with Linear Models the Easy Way

Starting to Combine Variables. 258
Mixing Variables of Different Types. 264
Switching to Probabilities. 267
Specifying a binary response. 267
Handling multiple classes. 270
Guessing the Right Features . 271
Defining the outcome of features that don’t work together. 271
Solving overfitting by using selection. 272
Learning One Example at a Time . 274
Using gradient descent. 275
Understanding how SGD is different. 275

CHAPTER 16: Hitting Complexity with Neural Networks

Learning and Imitating from Nature .280
Going forth with feed-forward. 281
Going even deeper down the rabbit hole . 283
Getting Back with Backpropagation. 286
Struggling with Overfitting. 289
Understanding the problem . 289
Opening the black box. 290
Introducing Deep Learning . 293

CHAPTER 17: Going a Step beyond Using Support

Vector Machines. 297
Revisiting the Separation Problem: A New Approach . 298
Explaining the Algorithm . 299
Getting into the math of an SVM. 301
Avoiding the pitfalls of nonseparability. 302
Applying Nonlinearity. 303
Demonstrating the kernel trick by example . 305
Discovering the different kernels. 306
Illustrating Hyper-Parameters. 308
Classifying and Estimating with SVM . 309

CHAPTER 18: Resorting to Ensembles of Learners

Leveraging Decision Trees. 316
Growing a forest of trees. 317
Understanding the importance measures. 321
Working with Almost Random Guesses. 324
Bagging predictors with Adaboost. 324
Boosting Smart Predictors. 327
Meeting again with gradient descent. 328
Averaging Different Predictors . 329


CHAPTER 19: Classifying Images

Working with a Set of Images . 334
Extracting Visual Features . 338
Recognizing Faces Using Eigenfaces. 340
Classifying Images. 343

CHAPTER 20: Scoring Opinions and Sentiments

Introducing Natural Language Processing. 349
Understanding How Machines Read . 350
Processing and enhancing text. 352
Scraping textual datasets from the web . 357
Handling problems with raw text. 360
Using Scoring and Classification. 362
Performing classification tasks. 362
Analyzing reviews from e-commerce. 365

CHAPTER 21: Recommending Products and Movies

Realizing the Revolution. 370
Downloading Rating Data. 371
Trudging through the MovieLens dataset. 371
Navigating through anonymous web data . 373
Encountering the limits of rating data. 374
Leveraging SVD . 375
Considering the origins of SVD. 376
Understanding the SVD connection. 377
Seeing SVD in action. 378


CHAPTER 22: Ten Machine Learning Packages to Master

Cloudera Oryx. 386
CUDA-Convnet. 386
ConvNetJS. 387
e1071. 387
gbm. 388
Gensim . 388
glmnet. 388
randomForest . 389
SciPy . 389
XGBoost . 390

CHAPTER 23: Ten Ways to Improve Your Machine

Learning Models. 391
Studying Learning Curves .392
Using Cross-Validation Correctly. 393
Choosing the Right Error or Score Metric . 394
Searching for the Best Hyper-Parameters. 395
Testing Multiple Models. 395
Averaging Models. 396
Stacking Models. 396
Applying Feature Engineering. 397
Selecting Features and Examples. 397
Looking for More Data. 398

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