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 significance of genetic algorithms to data mining?A: With a growing number of data sources and hence an increase in volume, it is difficult to derive actionable insights from these data assets in reasonable time, despite exponentially growing computation power. We need smart algorithms to search through the solution space. Nature provides inspiration with the evolution of life on Earth. With the use of genetic algorithms we can greatly optimize the search and other data mining activities.Q: What are the basic components of a GA?A: Population initialization, fitness assignment, selection, crossover, mutation, and survivor selection are the basic components of a GA. We need to tune the parameter values for these components in order to find the solution in an optimized manner.