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 are the two basic categories of machine learning and how do they differ from each other?

A: Machine learning can be broadly categorized into supervised and unsupervised learning. In the case of supervised learning, the model is trained based on the historical data, which is treated as the version of truth, termed training data. In the case of unsupervised learning, the algorithm derives inferences based on the input data, without labeled training data. The hidden patterns within the datasets are derived on the fly. 

Q: Why is the Spark programming model suitable for machine learning with big datasets?

A: Spark is a general-purpose computation engine based on the fundamentals of distributed resilient computing. The large datasets are seamlessly distributed across cluster nodes for faster model generation and execution. Most of the underlying details are hidden from the data science engineer and hence there is a very limited learning curve involved in implementing...