Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
About This Book
Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

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Python Machine Learning : Learn How to Build Powerful Python Machine Learning Algorithms to Generate Useful Data Insights with This Data Analysis Tutorial

Python и машинное обучение: наука и искусство построения алгоритмов, которые извлекают знания из данных

Python Machine Learning, 1st Edition

Себастьян Рашка; перевод с англ. А. В. Логунова

Adobe InDesign CS5.5 (7.5.3)

Sebastian Raschka

Рашка, Себастьян

Academic Press, Incorporated

Morgan Kaufmann Publishers

Jackdaw Publications Ltd

Constable and Robinson

Reprint Services Corp.

Vintage Digital

Brooks/Cole

ДМК Пресс

Community experience distilled, Community experience distilled, England, 2016

Community experience distilled, Birmingham, UK, 2015

United Kingdom and Ireland, United Kingdom

United States, United States of America

Цветное издание, Москва, Russia, 2017

1st edition, 2015

lg2867141

producers:
Adobe PDF Library 9.9

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Cover......Page 1
Copyright......Page 3
Credits......Page 4
Foreword......Page 6
About the Author......Page 7
About the Reviewers......Page 8
www.PacktPub.com......Page 10
Table of Contents......Page 12
Preface......Page 18
Chapter 1: Giving Computers the Ability to Learn from Data......Page 26
The three different types of machine learning......Page 27
Classification for predicting class labels......Page 28
Regression for predicting continuous outcomes......Page 29
Discovering hidden structures with unsupervised learning......Page 31
Dimensionality reduction for data compression......Page 32
An introduction to the basic terminology and notations......Page 33
A roadmap for building machine learning systems......Page 35
Preprocessing – getting data into shape......Page 36
Training and selecting a predictive model......Page 37
Installing Python packages......Page 38
Summary......Page 40
Chapter 2: Training Machine Learning Algorithms for Classification......Page 42
Artificial neurons – a brief glimpse into the early history of machine learning......Page 43
Implementing a perceptron learning algorithm in Python......Page 49
Training a perceptron model on the Iris dataset......Page 52
Adaptive linear neurons and the convergence of learning......Page 58
Minimizing cost functions with gradient descent......Page 59
Implementing an Adaptive Linear Neuron in Python......Page 61
Large scale machine learning and stochastic gradient descent......Page 67
Summary......Page 72
Choosing a classification algorithm......Page 74
Training a perceptron via scikit-learn......Page 75
Logistic regression intuition and conditional probabilities......Page 81
Learning the weights of the logistic cost function......Page 84
Training a logistic regression model with scikit-learn......Page 87
Tackling overfitting via regularization......Page 90
Maximum margin classification with support vector machines......Page 94
Maximum margin intuition......Page 95
Dealing with the nonlinearly separable case using slack variables......Page 96
Alternative implementations in scikit-learn......Page 99
Solving nonlinear problems using a kernel SVM......Page 100
Using the kernel trick to find separating hyperplanes in higher dimensional space......Page 102
Decision tree learning......Page 105
Maximizing information gain – getting the most bang for the buck......Page 107
Building a decision tree......Page 113
Combining weak to strong learners via random forests......Page 115
K-nearest neighbors – a lazy learning algorithm......Page 117
Summary......Page 121
Dealing with missing data......Page 124
Eliminating samples or features with missing values......Page 126
Understanding the scikit-learn estimator API......Page 127
Mapping ordinal features......Page 129
Encoding class labels......Page 130
Performing one-hot encoding on nominal features......Page 131
Partitioning a dataset in training and test sets......Page 133
Bringing features onto the same scale......Page 135
Sparse solutions with L1 regularization......Page 137
Sequential feature selection algorithms......Page 143
Assessing feature importance with random forests......Page 149
Summary......Page 151
Chapter 5: Compressing Data via Dimensionality Reduction......Page 152
Unsupervised dimensionality reduction via principal component analysis......Page 153
Total and explained variance......Page 155
Feature transformation......Page 158
Principal component analysis in scikit-learn......Page 160
Supervised data compression via linear discriminant analysis......Page 163
Computing the scatter matrices......Page 165
Selecting linear discriminants for the new feature subspace......Page 168
Projecting samples onto the new feature space......Page 170
LDA via scikit-learn......Page 171
Kernel functions and the kernel trick......Page 173
Implementing a kernel principal component analysis in Python......Page 179
Example 1 – separating half-moon shapes......Page 180
Example 2 – separating concentric circles......Page 184
Projecting new data points......Page 187
Kernel principal component analysis in scikit-learn......Page 191
Summary......Page 192
Streamlining workflows with pipelines......Page 194
Loading the Breast Cancer Wisconsin dataset......Page 195
Combining transformers and estimators in a pipeline......Page 196
The holdout method......Page 198
K-fold cross-validation......Page 200
Debugging algorithms with learning and validation curves......Page 204
Diagnosing bias and variance problems with learning curves......Page 205
Addressing overfitting and underfitting with validation curves......Page 208
Fine-tuning machine learning models via grid search......Page 210
Tuning hyperparameters via grid search......Page 211
Algorithm selection with nested cross-validation......Page 212
Looking at different performance evaluation metrics......Page 214
Reading a confusion matrix......Page 215
Optimizing the precision and recall of a classification model......Page 216
Plotting a receiver operating characteristic......Page 218
The scoring metrics for multiclass classification......Page 222
Summary......Page 223
Learning with ensembles......Page 224
Implementing a simple majority vote classifier......Page 228
Combining different algorithms for classification with majority vote......Page 235
Evaluating and tuning the ensemble classifier......Page 238
Bagging – building an ensemble of classifiers from bootstrap samples......Page 244
Leveraging weak learners via adaptive boosting......Page 249
Summary......Page 257
Obtaining the IMDb movie review dataset......Page 258
Transforming words into feature vectors......Page 261
Assessing word relevancy via term frequency-inverse document frequency......Page 263
Cleaning text data......Page 265
Processing documents into tokens......Page 267
Training a logistic regression model for document classification......Page 269
Working with bigger data – online algorithms and out-of-core learning......Page 271
Summary......Page 275
Chapter 9: Embedding a Machine Learning Model into a Web Application......Page 276
Serializing fitted scikit-learn estimators......Page 277
Setting up a SQLite database for data storage......Page 280
Developing a web application with Flask......Page 282
Our first Flask web application......Page 283
Form validation and rendering......Page 284
Turning the movie classifier into a web application......Page 289
Deploying the web application to a public server......Page 297
Updating the movie review classifier......Page 299
Summary......Page 301
Chapter 10: Predicting Continuous Target Variables with Regression Analysis ......Page 302
Introducing a simple linear regression model......Page 303
Exploring the Housing Dataset......Page 304
Visualizing the important characteristics of a dataset......Page 305
Solving regression for regression parameters with gradient descent......Page 310
Estimating the coefficient of a regression model via scikit-learn......Page 314
Fitting a robust regression model using RANSAC......Page 316
Evaluating the performance of linear regression models......Page 319
Using regularized methods for regression......Page 322
Turning a linear regression model into a curve – polynomial regression......Page 323
Modeling nonlinear relationships in the Housing Dataset......Page 325
Decision tree regression......Page 329
Random forest regression......Page 331
Summary......Page 334
Chapter 11: Working with Unlabeled Data – Clustering Analysis......Page 336
Grouping objects by similarity using k-means......Page 337
K-means++......Page 340
Hard versus soft clustering......Page 342
Using the elbow method to find the optimal number of clusters......Page 345
Quantifying the quality of clustering via silhouette plots......Page 346
Organizing clusters as a hierarchical tree......Page 351
Performing hierarchical clustering on a distance matrix......Page 353
Attaching dendrograms to a heat map......Page 357
Locating regions of high density via DBSCAN......Page 359
Summary......Page 365
Chapter 12: Training Artificial Neural Networks for Image Recognition......Page 366
Modeling complex functions with artificial neural networks......Page 367
Single-layer neural network recap......Page 368
Introducing the multi-layer neural network architecture......Page 370
Activating a neural network via forward propagation......Page 372
Classifying handwritten digits......Page 375
Obtaining the MNIST dataset......Page 376
Implementing a multi-layer perceptron......Page 381
Computing the logistic cost function......Page 390
Training neural networks via backpropagation......Page 393
Developing your intuition for backpropagation......Page 397
Debugging neural networks with gradient checking......Page 398
Convergence in neural networks......Page 404
Convolutional Neural Networks......Page 406
Recurrent Neural Networks......Page 408
A few last words about neural network implementation......Page 409
Summary......Page 410
Chapter 13: Parallelizing Neural Network Training with Theano......Page 412
Building, compiling, and running expressions with Theano......Page 413
What is Theano?......Page 415
First steps with Theano......Page 416
Configuring Theano......Page 417
Working with array structures......Page 419
Wrapping things up – a linear regression example......Page 422
Choosing activation functions for feedforward neural networks......Page 426
Logistic function recap......Page 427
Estimating probabilities in multi-class classification via the softmax function......Page 429
Broadening the output spectrum by using a hyperbolic tangent......Page 430
Training neural networks efficiently using Keras......Page 433
Summary......Page 439
Index......Page 442

Cover 1
Copyright 3
Credits 4
Foreword 6
About the Author 7
About the Reviewers 8
www.PacktPub.com 10
Table of Contents 12
Preface 18
Chapter 1: Giving Computers the Ability to Learn from Data 26
Building intelligent machines to transform data into knowledge 27
The three different types of
machine learning 27
Making predictions about the future with supervised learning 28
Classification for predicting class labels 28
Regression for predicting continuous outcomes 29
Solving interactive problems with reinforcement learning 31
Discovering hidden structures with unsupervised learning 31
Finding subgroups with clustering 32
Dimensionality reduction for data compression 32
An introduction to the basic terminology and notations 33
A roadmap for building machine learning systems 35
Preprocessing – getting data into shape 36
Training and selecting a predictive model 37
Evaluating models and predicting unseen data instances 38
Using Python for machine learning 38
Installing Python packages 38
Summary 40
Chapter 2: Training Machine Learning Algorithms for Classification 42
Artificial neurons – a brief glimpse into the early history of machine learning 43
Implementing a perceptron learning algorithm in Python 49
Training a perceptron model on the Iris dataset 52
Adaptive linear neurons and the convergence of learning 58
Minimizing cost functions with gradient descent 59
Implementing an Adaptive Linear Neuron in Python 61
Large scale machine learning and stochastic gradient descent 67
Summary 72
Chapter 3: A Tour of Machine Learning Classifiers Using Scikit-learn 74
Choosing a classification algorithm 74
First steps with scikit-learn 75
Training a perceptron via scikit-learn 75
Modeling class probabilities via logistic regression 81
Logistic regression intuition and conditional probabilities 81
Learning the weights of the logistic cost function 84
Training a logistic regression model with scikit-learn 87
Tackling overfitting via regularization 90
Maximum margin classification with support vector machines 94
Maximum margin intuition 95
Dealing with the nonlinearly separable case using slack variables 96
Alternative implementations in scikit-learn 99
Solving nonlinear problems using a kernel SVM 100
Using the kernel trick to find separating hyperplanes in higher dimensional space 102
Decision tree learning 105
Maximizing information gain – getting the most bang for the buck 107
Building a decision tree 113
Combining weak to strong learners via random forests 115
K-nearest neighbors – a lazy learning algorithm 117
Summary 121
Chapter 4: Building Good Training Sets – Data Preprocessing 124
Dealing with missing data 124
Eliminating samples or features with missing values 126
Imputing missing values 127
Understanding the scikit-learn estimator API 127
Handling categorical data 129
Mapping ordinal features 129
Encoding class labels 130
Performing one-hot encoding on nominal features 131
Partitioning a dataset in training and test sets 133
Bringing features onto the same scale 135
Selecting meaningful features 137
Sparse solutions with L1 regularization 137
Sequential feature selection algorithms 143
Assessing feature importance with random forests 149
Summary 151
Chapter 5: Compressing Data via Dimensionality Reduction 152
Unsupervised dimensionality reduction via principal component analysis 153
Total and explained variance 155
Feature transformation 158
Principal component analysis in scikit-learn 160
Supervised data compression via linear discriminant analysis 163
Computing the scatter matrices 165
Selecting linear discriminants for the new feature subspace 168
Projecting samples onto the new feature space 170
LDA via scikit-learn 171
Using kernel principal component analysis for nonlinear mappings 173
Kernel functions and the kernel trick 173
Implementing a kernel principal component analysis in Python 179
Example 1 – separating half-moon shapes 180
Example 2 – separating concentric circles 184
Projecting new data points 187
Kernel principal component analysis in
scikit-learn 191
Summary 192
Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning 194
Streamlining workflows with pipelines 194
Loading the Breast Cancer Wisconsin dataset 195
Combining transformers and estimators in a pipeline 196
Using k-fold cross-validation to assess model performance 198
The holdout method 198
K-fold cross-validation 200
Debugging algorithms with learning and validation curves 204
Diagnosing bias and variance problems with learning curves 205
Addressing overfitting and underfitting with validation curves 208
Fine-tuning machine learning models via grid search 210
Tuning hyperparameters via grid search 211
Algorithm selection with nested
cross-validation 212
Looking at different performance evaluation metrics 214
Reading a confusion matrix 215
Optimizing the precision and recall of a classification model 216
Plotting a receiver operating characteristic 218
The scoring metrics for multiclass classification 222
Summary 223
Chapter 7: Combining Different Models for Ensemble Learning 224
Learning with ensembles 224
Implementing a simple majority vote classifier 228
Combining different algorithms for classification with majority vote 235
Evaluating and tuning the ensemble classifier 238
Bagging – building an ensemble of classifiers from bootstrap samples 244
Leveraging weak learners via adaptive boosting 249
Summary 257
Chapter 8: Applying Machine Learning to Sentiment Analysis 258
Obtaining the IMDb movie review dataset 258
Introducing the bag-of-words model 261
Transforming words into feature vectors 261
Assessing word relevancy via term frequency-inverse document frequency 263
Cleaning text data 265
Processing documents into tokens 267
Training a logistic regression model for document classification 269
Working with bigger data – online algorithms and out-of-core learning 271
Summary 275
Chapter 9: Embedding a Machine Learning Model into a Web Application 276
Serializing fitted scikit-learn estimators 277
Setting up a SQLite database for data storage 280
Developing a web application with Flask 282
Our first Flask web application 283
Form validation and rendering 284
Turning the movie classifier into a web application 289
Deploying the web application to a public server 297
Updating the movie review classifier 299
Summary 301
Chapter 10: Predicting Continuous Target Variables with Regression Analysis 302
Introducing a simple linear regression model 303
Exploring the Housing Dataset 304
Visualizing the important characteristics of a dataset 305
Implementing an ordinary least squares linear regression model 310
Solving regression for regression parameters with gradient descent 310
Estimating the coefficient of a regression model via scikit-learn 314
Fitting a robust regression model using RANSAC 316
Evaluating the performance of linear regression models 319
Using regularized methods for regression 322
Turning a linear regression model into a curve – polynomial regression 323
Modeling nonlinear relationships in the Housing Dataset 325
Dealing with nonlinear relationships using random forests 329
Decision tree regression 329
Random forest regression 331
Summary 334
Chapter 11 : Working with Unlabeled Data – Clustering Analysis 336
Grouping objects by similarity using k-means 337
K-means++ 340
Hard versus soft clustering 342
Using the elbow method to find the optimal number of clusters 345
Quantifying the quality of clustering via silhouette plots 346
Organizing clusters as a hierarchical tree 351
Performing hierarchical clustering on a distance matrix 353
Attaching dendrograms to a heat map 357
Applying agglomerative clustering via
scikit-learn 359
Locating regions of high density via DBSCAN 359
Summary 365
Chapter 12: Training Artificial Neural Networks for Image Recognition 366
Modeling complex functions with artificial neural networks 367
Single-layer neural network recap 368
Introducing the multi-layer neural network architecture 370
Activating a neural network via forward propagation 372
Classifying handwritten digits 375
Obtaining the MNIST dataset 376
Implementing a multi-layer perceptron 381
Training an artificial neural network 390
Computing the logistic cost function 390
Training neural networks via backpropagation 393
Developing your intuition for backpropagation 397
Debugging neural networks with gradient checking 398
Convergence in neural networks 404
Other neural network architectures 406
Convolutional Neural Networks 406
Recurrent Neural Networks 408
A few last words about neural network implementation 409
Summary 410
Chapter 13: Parallelizing Neural Network Training with Theano 412
Building, compiling, and running expressions with Theano 413
What is Theano? 415
First steps with Theano 416
Configuring Theano 417
Working with array structures 419
Wrapping things up – a linear regression example 422
Choosing activation functions for feedforward neural networks 426
Logistic function recap 427
Estimating probabilities in multi-class classification via the softmax function 429
Broadening the output spectrum by using a hyperbolic tangent 430
Training neural networks efficiently using Keras 433
Summary 439
Index 442

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsBook DescriptionMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. What you will learnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataWho this book is forIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask -- and answer -- tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning -- whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som..

Link to the GitHub Repository containing the code examples and additional material:(https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

2020-11-29