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

Regression analysis


Regression analysis is a statistical modeling technique that is used for predicting or forecasting the occurrence of an event or the value of a continuous variable (dependent variable), based on the value of one or many independent variables. For example, when we want to drive from one place to another, there are numerous factors that affect the amount of time it will take to reach the destination, for example, the start time, distance, real-time traffic conditions, construction activities on the road, and weather conditions. All these factors impact the actual time it will take to reach the destination. As you can imagine, some factors have more impact than the others on the value of the dependent variable. In regression analysis, we mathematically sort out which variables impact the outcome, leading us to understand which factors matter most, which ones do not impact the outcome in a meaningful way, how these factors relate to each other, and mathematically, the quantified...