Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

Building machine learning systems with Python explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your...

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Detalles Bibliográficos
Otros Autores: Coelho, Luis Pedro, author (author), Richert, Wilhelm, author, Brucher, Matthieu, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt July 2018.
Edición:Third edition
Materias:
Python (Computer program language)
Machine learning.
Artificial intelligence.
Python (Llenguatge de programació)
Aprenentatge automàtic
Intel·ligència artificial
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630734206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Python Machine Learning
  • Machine learning and Python - a dream team
  • What the book will teach you - and what it will not
  • How to best read this book
  • What to do when you are stuck
  • Getting started
  • Introduction to NumPy, SciPy, Matplotlib, and TensorFlow
  • Installing Python
  • Chewing data efficiently with NumPy and intelligently with SciPy
  • Learning NumPy
  • Indexing
  • Handling nonexistent values
  • Comparing the runtime
  • Learning SciPy
  • Fundamentals of machine learning
  • Asking a question
  • Getting answers
  • Our first (tiny) application of machine learning
  • Reading in the data
  • Preprocessing and cleaning the data
  • Choosing the right model and learning algorithm
  • Before we build our first model
  • Starting with a simple straight line
  • Toward more complex models
  • Stepping back to go forward - another look at our data
  • Training and testing
  • Answering our initial question
  • Summary
  • Chapter 2: Classifying with Real-World Examples
  • The Iris dataset
  • Visualization is a good first step
  • Classifying with scikit-learn
  • Building our first classification model
  • Evaluation - holding out data and cross-validation
  • How to measure and compare classifiers
  • A more complex dataset and the nearest-neighbor classifier
  • Learning about the seeds dataset
  • Features and feature engineering
  • Nearest neighbor classification
  • Looking at the decision boundaries
  • Which classifier to use
  • Summary
  • Chapter 3: Regression
  • Predicting house prices with regression
  • Multidimensional regression
  • Cross-validation for regression
  • Penalized or regularized regression
  • L1 and L2 penalties
  • Using Lasso or ElasticNet in scikit-learn
  • Visualizing the Lasso path.
  • P-greater-than-N scenarios
  • An example based on text documents
  • Setting hyperparameters in a principled way
  • Regression with TensorFlow
  • Summary
  • Chapter 4: Classification I - Detecting Poor Answers
  • Sketching our roadmap
  • Learning to classify classy answers
  • Tuning the instance
  • Tuning the classifier
  • Fetching the data
  • Slimming the data down to chewable chunks
  • Preselecting and processing attributes
  • Defining what a good answer is
  • Creating our first classifier
  • Engineering the features
  • Training the classifier
  • Measuring the classifier's performance
  • Designing more features
  • Deciding how to improve the performance
  • Bias, variance and their trade-off
  • Fixing high bias
  • Fixing high variance
  • High or low bias?
  • Using logistic regression
  • A bit of math with a small example
  • Applying logistic regression to our post-classification problem
  • Looking behind accuracy - precision and recall
  • Slimming the classifier
  • Ship it!
  • Classification using Tensorflow
  • Summary
  • Chapter 5: Dimensionality Reduction
  • Sketching our roadmap
  • Selecting features
  • Detecting redundant features using filters
  • Correlation
  • Mutual information
  • Asking the model about the features using wrappers
  • Other feature selection methods
  • Feature projection
  • Principal component analysis
  • Sketching PCA
  • Applying PCA
  • Limitations of PCA and how LDA can help
  • Multidimensional scaling
  • Autoencoders, or neural networks for dimensionality reduction
  • Summary
  • Chapter 6: Clustering - Finding Related Posts
  • Measuring the relatedness of posts
  • How not to do it
  • How to do it
  • Preprocessing - similarity measured as a similar number of common words
  • Converting raw text into a bag of words
  • Counting words
  • Normalizing word count vectors
  • Removing less important words
  • Stemming.
  • Installing and using NLTK
  • Extending the vectorizer with NLTK's stemmer
  • Stop words on steroids
  • Our achievements and goals
  • Clustering
  • K-means
  • Getting test data to evaluate our ideas
  • Clustering posts
  • Solving our initial challenge
  • Another look at noise
  • Tweaking the parameters
  • Summary
  • Chapter 7: Recommendations
  • Rating predictions and recommendations
  • Splitting into training and testing
  • Normalizing the training data
  • A neighborhood approach to recommendations
  • A regression approach to recommendations
  • Combining multiple methods
  • Basket analysis
  • Obtaining useful predictions
  • Analyzing supermarket shopping baskets
  • Association rule mining
  • More advanced basket analysis
  • Summary
  • Chapter 8: Artificial Neural Networks and Deep Learning
  • Using TensorFlow
  • TensorFlow API
  • Graphs
  • Sessions
  • Useful operations
  • Saving and restoring neural networks
  • Training neural networks
  • Convolutional neural networks
  • Recurrent neural networks
  • LSTM for predicting text
  • LSTM for image processing
  • Summary
  • Chapter 9: Classification II - Sentiment Analysis
  • Sketching our roadmap
  • Fetching the Twitter data
  • Introducing the Naïve Bayes classifier
  • Getting to know the Bayes theorem
  • Being naïve
  • Using Naïve Bayes to classify
  • Accounting for unseen words and other oddities
  • Accounting for arithmetic underflows
  • Creating our first classifier and tuning it
  • Solving an easy problem first
  • Using all classes
  • Tuning the classifier's parameters
  • Cleaning tweets
  • Taking the word types into account
  • Determining the word types
  • Successfully cheating using SentiWordNet
  • Our first estimator
  • Putting everything together
  • Summary
  • Chapter 10: Topic Modeling
  • Latent Dirichlet allocation
  • Building a topic model
  • Comparing documents by topic.
  • Modeling the whole of Wikipedia
  • Choosing the number of topics
  • Summary
  • Chapter 11: Classification III - Music Genre Classification
  • Sketching our roadmap
  • Fetching the music data
  • Converting into WAV format
  • Looking at music
  • Decomposing music into sine-wave components
  • Using FFT to build our first classifier
  • Increasing experimentation agility
  • Training the classifier
  • Using a confusion matrix to measure accuracy in multiclass problems
  • An alternative way to measure classifier performance using receiver-operator characteristics
  • Improving classification performance with mel frequency cepstral coefficients
  • Music classification using Tensorflow
  • Summary
  • Chapter 12: Computer Vision
  • Introducing image processing
  • Loading and displaying images
  • Thresholding
  • Gaussian blurring
  • Putting the center in focus
  • Basic image classification
  • Computing features from images
  • Writing your own features
  • Using features to find similar images
  • Classifying a harder dataset
  • Local feature representations
  • Image generation with adversarial networks
  • Summary
  • Chapter 13: Reinforcement Learning
  • Types of reinforcement learning
  • Policy and value network
  • Q-network
  • Excelling at games
  • A small example
  • Using Tensorflow for the text game
  • Playing breakout
  • Summary
  • Chapter 14: Bigger Data
  • Learning about big data
  • Using jug to break up your pipeline into tasks
  • An introduction to tasks in jug
  • Looking under the hood
  • Using jug for data analysis
  • Reusing partial results
  • Using Amazon Web Services
  • Creating your first virtual machines
  • Installing Python packages on Amazon Linux
  • Running jug on our cloud machine
  • Automating the generation of clusters with cfncluster
  • Summary
  • Appendi A: Where to Learn More About Machine Learning
  • Online courses
  • Books
  • Blogs.
  • Data sources
  • Getting competitive
  • All that was left out
  • Summary
  • Other Books You May Enjoy
  • Index.

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