In the previous chapter, we established a basic foundation for our journey toward building intelligent systems. We differentiated the machine learning algorithms in two primary groups of supervised and unsupervised algorithms, and explored how the Spark programming model is a handy tool for us to implement these algorithms with a simple programming interface, along with a brief overview of the machine learning libraries available in Spark. We have also covered the fundamentals of regression analysis with a simple example and supporting code in Spark ML. The chapter showed how to cluster the data using the K-means algorithm and a deep dive into the realm of dimensionality reduction, which primarily helps us in representing the same information with fewer dimensions without any loss of information. We have formed the basis for the implementation of the recommendation engines with an understanding of principal component analysis, content-based filtering...
Artificial Intelligence for Big Data
By :
Artificial Intelligence for Big Data
By:
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.
Related Content you might be interested in
Current Title:
Artificial Intelligence for Big Data
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
Big Data and Artificial Intelligence Systems
Ontology for Big Data
Learning from Big Data
Neural Network for Big Data
Deep Big Data Analytics
Natural Language Processing
Fuzzy Systems
Genetic Programming
Swarm Intelligence
Reinforcement Learning
Cyber Security
Cognitive Computing
Other Books You May Enjoy
Index
Customer Reviews