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

Chapter 10. Reinforcement Learning

In Chapter 3Learning from Big Data, we were introduced to two fundamental types of machine learning techniques: supervised learning and unsupervised learning. In case of the supervised learning, a model is trained based on the historical data (observations) for predicting the outcomes based on the new data inputs. In the case of unsupervised learning, the model tries to derive patterns within the datasets and define logical grouping boundaries in order to separate the solution space. There is a third type of machine learning algorithm that is equally important for the evolution of artificial intelligence.

Remember the process of learning to ride a bicycle. We observe another person who is riding a bicycle, create a mental model on how to do it, and attempt it ourselves. It is not possible to just get the balancing and movement on a bicycle right in the first attempt. We (actor) try for the first time (action) on the road (environment) and may fall down...