Book Image

Python Machine Learning By Example - Second Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Second Edition

By: Yuxi (Hayden) Liu

Overview of this book

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Fundamentals of Machine Learning
3
Section 2: Practical Python Machine Learning By Example
12
Section 3: Python Machine Learning Best Practices

Topic modeling using LDA

Let's explore another popular topic modeling algorithm, latent Dirichlet allocation (LDA). LDA is a generative probabilistic graphical model that explains each input document by means of a mixture of topics with certain probabilities. Again, topic in topic modeling means a collection of words with a certain connection. In other words, LDA basically deals with two probability values, P(term | topic) and P(topic | document). This can be difficult to understand at the beginning. So, let's start from the bottom, the end result of an LDA model.

Let's take a look at the following set of documents:

Document 1: This restaurant is famous for fish and chips.
Document 2: I had fish and rice for lunch.
Document 3: My sister bought me a cute kitten.
Document 4: Some research shows eating too much rice is bad.
Document 5: I always forget to feed fish to my...