Welcome to course four of the big data specialization. I'm Ilkay Altintas, for the new learners I'm the Chief Data Science Officer at the San Diego Supercomputer Center at the University of California, San Diego. I feel honored to teach you the basics of big data modeling, management, and analysis in this specialization. And to work with Dr. Mai Nguyen on this class. >> And I'm Mai Nguyen, while most of you might be familiar with Ilkay, I'm a new face. I'm excited to be here to teach you what I love doing, machine learning. I received my PhD in computer science with a focus on machine learning from the University of California in San Diego. Since then, I have worked as a data scientist, and instructor of machine learning in various venues. I am the Lead for Data Analytics at SDFC. In this role, I work on data science projects doing research on scalability on machine learning methods to big data. >> We are really happy to have you in this course to develop your understanding and skills in machine learning. >> And give you an introductory level experience with application of machine learning to big data. By now you might have just finished our first three courses and learned the basics of big data modeling, management, integration, and processing. If you haven't, it's not required. But for those with less background in big data management and systems, you might find it valuable. >> We understand that you may not even have heard anything on machine learning yet. That's why we will start by discussing what machine learning is. Describing some sample applications and presenting the typical process of a machine learning project to give you a sense of what machine learning is. Then we will delve into some commonly used machine learning techniques like classification and clustering. >> We are also going to show you how to explore your data, prepare it for analysis, and evaluate the results you get with your machine learning model. These are all necessary steps for a successful machine learning solution. >> As you know, for many data science applications one has to use many different tools and methods to analyze data. In fact, keeping up with the rapid development of new tools is one of the challenges of today's big data environment. In this course, we will introduce you to two different types of machine learning tools namely Nime and Spark MNL. Nime is a graphical user interface based tool that requires no programming. And as representative of a set of tools used in visual workflow approach to machine learning. You will have hand on practice with Nime as you go through the exercises in this course. We are also excited to show you examples of data processing using Sparks Machine Learning library and OwlWeb. Our goal here is to provide you with simple hands-on exercises that require introductory level programming, to inspire you on how big data machine learning tools can be operated. We wish you a fun time learning and hope to hear from you in the discussion forums and learner stories as always. >> We have suggested time estimates each week for the course. But feel free to take the course at a faster or slower pace. And don't forget to connect to other learners through the forums to enhance your learning experience. >> Happy learning. >> Happy learning.