Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Table of Contents (9 chapters)

News Topic Classification with Support Vector Machine

This chapter continues our journey of classifying text data, a great starting point of learning machine learning classification with broad real-life applications. We will be focusing on topic classification on the news data we used in Chapter 2, Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms and using another powerful classifier, support vector machine, to solve such problems.

We will get into details for the topics mentioned:

  • Term frequency-inverse document frequency
  • Support vector machine
  • The mechanics of SVM
  • The implementations of SVM
  • Multiclass classification strategies
  • The nonlinear kernels of SVM
  • Choosing between linear and Gaussian kernels
  • Overfitting and reducing overfitting in SVM
  • News topic classification with SVM
  • Tuning with grid search and cross-validation