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Building machine learning systems with Python : get more from your data through creating practical machine learning systems with Python /
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing,
2015.
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| Edición: | Second edition. |
| Colección: | Community experience distilled.
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| 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
- a 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 nonexisting values; Comparing the runtime; Learning SciPy; Our first (tiny) application of machine learning
- 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: Classifying with Real-world Examples; The Iris dataset; Visualization is a good first step; 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; Classifying with scikit-learn; Looking at the decision boundaries; 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 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
- Stop words on steroidsOur achievements and goals; Clustering; K-means; 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; Building a topic model; Comparing documents by topics; 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 kNN; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features; Deciding how to improve; Bias-variance and their tradeoff; 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 post classification problem; Looking behind accuracy
- precision and recall