Book Image

Artificial Intelligence with Python

Book Image

Artificial Intelligence with Python

Overview of this book

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more. Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.
Table of Contents (23 chapters)
Artificial Intelligence with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Generating data using Hidden Markov Models


A Hidden Markov Model (HMM) is a powerful analysis technique for analyzing sequential data. It assumes that the system being modeled is a Markov process with hidden states. This means that the underlying system can be one among a set of possible states. It goes through a sequence of state transitions, thereby producing a sequence of outputs. We can only observe the outputs but not the states. Hence these states are hidden from us. Our goal is to model the data so that we can infer the state transitions of unknown data.

In order to understand HMMs, let's consider the example of a salesman who has to travel between the following three cities for his job -- London, Barcelona, and New York. His goal is to minimize the traveling time so that he can be more efficient. Considering his work commitments and schedule, we have a set of probabilities that dictate the chances of going from city X to city Y. In the information given below, P(X -> Y) indicates...