Cargando…
Building Machine Learning Systems with Python.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2013.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python
- the dream team; What the book will teach you (and what it will not); What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, and Matplotlib; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling non-existing values; Comparing runtime behaviors; Learning SciPy; Our first (tiny) machine learning application.
- Reading in the dataPreprocessing and cleaning the data; Choosing the right model and learning algorithm; Before building our first model; Starting with a simple straight line; Towards some advanced stuff; Stepping back to go forward
- another look at our data; Training and testing; Answering our initial question; Summary; Chapter 2: Learning How to Classify with Real-world Examples; The Iris dataset; The first step is visualization; Building our first classification model; Evaluation
- holding out data and cross-validation; Building more complex classifiers.
- A more complex dataset and a more complex classifierLearning about the Seeds dataset; Features and feature engineering; Nearest neighbor classification; Binary and multiclass classification; Summary; Chapter 3: Clustering
- Finding Related Posts; Measuring the relatedness of posts; How not to do it; How to do it; Preprocessing
- similarity measured as similar number of common words; Converting raw text into a bag-of-words; Counting words; Normalizing the word count vectors; Removing less important words; Stemming; Installing and using NLTK; Extending the vectorizer with NLTK's stemmer.
- Stop words on steroidsOur achievements and goals; Clustering; KMeans; Getting test data to evaluate our ideas on; Clustering posts; Solving our initial challenge; Another look at noise; Tweaking the parameters; Summary; Chapter 4: Topic Modeling; Latent Dirichlet allocation (LDA); Building a topic model; Comparing similarity in topic space; Modeling the whole of Wikipedia; Choosing the number of topics; Summary; Chapter 5: Classification
- 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 chunksPreselection and processing of attributes; Defining what is a good answer; Creating our first classifier; Starting with the k-nearest neighbor (kNN) algorithm; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features; Deciding how to improve; Bias-variance and its trade-off; Fixing high bias; Fixing high variance; High bias or low bias; Using logistic regression; A bit of math with a small example; Applying logistic regression to our postclassification problem.
- Looking behind accuracy
- precision and recall.