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Python Machine Learning

Python Machine Learning

By : Sebastian Raschka
4.3 (100)
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Python Machine Learning

Python Machine Learning

4.3 (100)
By: Sebastian Raschka

Overview of this book

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.
Table of Contents (15 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Giving Computers the Ability to Learn from Data
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1. Giving Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
An introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
2
2. Training Machine Learning Algorithms for Classification
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
Adaptive linear neurons and the convergence of learning
Summary
3
3. A Tour of Machine Learning Classifiers Using Scikit-learn
3. A Tour of Machine Learning Classifiers Using Scikit-learn
Choosing a classification algorithm
First steps with scikit-learn
Modeling class probabilities via logistic regression
Maximum margin classification with support vector machines
Solving nonlinear problems using a kernel SVM
Decision tree learning
K-nearest neighbors – a lazy learning algorithm
Summary
4
4. Building Good Training Sets – Data Preprocessing
4. Building Good Training Sets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset in training and test sets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
5
5. Compressing Data via Dimensionality Reduction
5. Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
Supervised data compression via linear discriminant analysis
Using kernel principal component analysis for nonlinear mappings
Summary
6
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Streamlining workflows with pipelines
Using k-fold cross-validation to assess model performance
Debugging algorithms with learning and validation curves
Fine-tuning machine learning models via grid search
Looking at different performance evaluation metrics
Summary
7
7. Combining Different Models for Ensemble Learning
7. Combining Different Models for Ensemble Learning
Learning with ensembles
Implementing a simple majority vote classifier
Evaluating and tuning the ensemble classifier
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Summary
8
8. Applying Machine Learning to Sentiment Analysis
8. Applying Machine Learning to Sentiment Analysis
Obtaining the IMDb movie review dataset
Introducing the bag-of-words model
Training a logistic regression model for document classification
Working with bigger data – online algorithms and out-of-core learning
Summary
9
9. Embedding a Machine Learning Model into a Web Application
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
Turning the movie classifier into a web application
Deploying the web application to a public server
Summary
10
10. Predicting Continuous Target Variables with Regression Analysis
10. Predicting Continuous Target Variables with Regression Analysis
Introducing a simple linear regression model
Exploring the Housing Dataset
Implementing an ordinary least squares linear regression model
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
Summary
11
11. Working with Unlabeled Data – Clustering Analysis
11. Working with Unlabeled Data – Clustering Analysis
Grouping objects by similarity using k-means
Organizing clusters as a hierarchical tree
Locating regions of high density via DBSCAN
Summary
12
12. Training Artificial Neural Networks for Image Recognition
12. Training Artificial Neural Networks for Image Recognition
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
Developing your intuition for backpropagation
Debugging neural networks with gradient checking
Convergence in neural networks
Other neural network architectures
A few last words about neural network implementation
Summary
13
13. Parallelizing Neural Network Training with Theano
13. Parallelizing Neural Network Training with Theano
Building, compiling, and running expressions with Theano
Choosing activation functions for feedforward neural networks
Training neural networks efficiently using Keras
Summary
14
Index
Index

An introduction to the basic terminology and notations

Now that we have discussed the three broad categories of machine learning—supervised, unsupervised, and reinforcement learning—let us have a look at the basic terminology that we will be using in the next chapters. The following table depicts an excerpt of the Iris dataset, which is a classic example in the field of machine learning. The Iris dataset contains the measurements of 150 iris flowers from three different species: Setosa, Versicolor, and Viriginica. Here, each flower sample represents one row in our data set, and the flower measurements in centimeters are stored as columns, which we also call the features of the dataset:

An introduction to the basic terminology and notations

To keep the notation and implementation simple yet efficient, we will make use of some of the basics of linear algebra. In the following chapters, we will use a matrix and vector notation to refer to our data. We will follow the common convention to represent each sample as separate row in a feature matrix An introduction to the basic terminology and notations, where each feature is stored as a separate column.

The Iris dataset, consisting of 150 samples and 4 features, can then be written as a An introduction to the basic terminology and notations matrix An introduction to the basic terminology and notations:

An introduction to the basic terminology and notations

Note

For the rest of this book, we will use the superscript (i) to refer to the ith training sample, and the subscript j to refer to the jth dimension of the training dataset.

We use lower-case, bold-face letters to refer to vectors An introduction to the basic terminology and notations and upper-case, bold-face letters to refer to matrices, respectively An introduction to the basic terminology and notations. To refer to single elements in a vector or matrix, we write the letters in italics (An introduction to the basic terminology and notations or An introduction to the basic terminology and notations, respectively).

For example, An introduction to the basic terminology and notations refers to the first dimension of flower sample 150, the sepal width. Thus, each row in this feature matrix represents one flower instance and can be written as four-dimensional column vector An introduction to the basic terminology and notations, An introduction to the basic terminology and notations.

Each feature dimension is a 150-dimensional row vector An introduction to the basic terminology and notations, for example:

An introduction to the basic terminology and notations

.

Similarly, we store the target variables (here: class labels) as a 150-dimensional column vector An introduction to the basic terminology and notations.

End of Section 4
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