Book description
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you’ll learn:
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills
Table of contents
- Preface
- 1. Introduction
- 2. Supervised Learning
- 3. Unsupervised Learning and Preprocessing
-
4. Representing Data and Engineering Features
- 4.1. Categorical Variables
- 4.2. OneHotEncoder and ColumnTransformer: Categorical Variables with scikit-learn
- 4.3. Convenient ColumnTransformer creation with make_columntransformer
- 4.4. Binning, Discretization, Linear Models, and Trees
- 4.5. Interactions and Polynomials
- 4.6. Univariate Nonlinear Transformations
- 4.7. Automatic Feature Selection
- 4.8. Utilizing Expert Knowledge
- 4.9. Summary and Outlook
- 5. Model Evaluation and Improvement
- 6. Algorithm Chains and Pipelines
-
7. Working with Text Data
- 7.1. Types of Data Represented as Strings
- 7.2. Example Application: Sentiment Analysis of Movie Reviews
- 7.3. Representing Text Data as a Bag of Words
- 7.4. Stopwords
- 7.5. Rescaling the Data with tf–idf
- 7.6. Investigating Model Coefficients
- 7.7. Bag-of-Words with More Than One Word (n-Grams)
- 7.8. Advanced Tokenization, Stemming, and Lemmatization
- 7.9. Topic Modeling and Document Clustering
- 7.10. Summary and Outlook
- 8. Wrapping Up
- Index
Product information
- Title: Introduction to Machine Learning with Python
- Author(s):
- Release date: September 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781449369897
You might also like
book
Fundamentals of Data Visualization
Effective visualization is the best way to communicate information from the increasingly large and complex datasets …
book
SQL for Data Analysis
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even …
book
Python Data Science Handbook, 2nd Edition
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, …
book
Building Machine Learning Powered Applications
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through …