Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python.
In this course, you’ll learn how to use the Spark MLlib. You’ll find out about the supervised and unsupervised ML algorithms. You’ll build classifications models, extracting proper futures from text using Word2Vect to achieve this. Next, we’ll build a Logistic Regression Model with Spark. Then we’ll find clusters and correlations in our data using K-Means clustering. We’ll learn how to validate models using cross-validation and area under the ROC measurement.
You’ll also build an effective Recommendation Model using distributed Spark algorithm. We will look at graph processing with GraphX library. By the end of the course, you’ll be able to focus on leveraging Spark to create fast and efficient machine learning programs.
About the author
Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservices architecture and functional programming. He dedicates considerable time and effort to be better every day. Recently, he’s been diving into Big Data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.
Tomasz thinks that we should always try to consider different solutions and approaches before solving a problem. Recently, he was a speaker at several conferences in Poland - Confitura and JDD (Java Developer’s Day) and also at Krakow Scala User Group.
He also conducted a live coding session at Geecon Conference.
Who is the target audience?
- This video course is for those who are familiar with machine learning and now want to use Spark to develop efficient and fast machine learning systems.
