Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Frequently asked questions


Q: What is the difference between supervised learning and reinforcement learning?

A: In the case of supervised learning algorithms, the model is trained based on historical data which describes the trend for the data historically and establishes a correlation between the event data and resultant output. In that case, the supervised learning model is a curve fitting exercise that maps the data points (independent variables) to a set of output variables (dependent variables). Availability of the historical data is essential for supervised learning. In case of reinforcement learning, the agent is modeled based on the rewards it receives based on the action(s) it takes within the context of the environment in which it is operating. There is no historical data available to the agent to train itself. However, a hybrid approach often works great where the agent is aware of the historical trends as well as applies exploration and exploitation strategies in order to maximize...