In this project, we will perform one of the most essential applications of machine learning – Customer Segmentation. We will implement customer segmentation in Apache Spark and Scala, whenever you need to find your best customer. Customer Segmentation is one of the most important applications of unsupervised learning. In this machine learning project, we will make use of K-means clustering which is the essential algorithm for clustering unlabeled datasets. Welcome to this project on Customer Segmentation using Apache Spark Machine Learning using Apache Zeppelin platform which allows you to execute your spark code in Apache Zeppelin notebook.

Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview.

Machine learning as a field has been around for a long time before deep neural networks took over the scene. Here are a list of the algorithms you need to know, so you can tackle any problem that comes your way. This isn't an exhaustive list, but your bases will be mostly covered. Also wanted to announce that my medium blog will be transitioning from general ML focus to a deep learning focus. Most of my work nowadays involves creating novel deep learning systems, so I want to spend more time writing about it!

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.