Training ML models is a compute-intensive operation and is best done in a distributed environment. This course will teach you how Spark can efficiently perform data explorations, cleaning, aggregations, and train ML models all on one platform.
Spark is possibly the most popular engine for big data processing these days. In this course, Building Machine Learning Models in Spark 2, you will learn to build and train Machine Learning (ML) models such as regression, classification, clustering, and recommendation systems on Spark 2.x's distributed processing environment.
This course starts off with an introduction of the 2 ML libraries available in Spark 2; the older spark.mllib library built on top of RDDs and the newer spark.ml library built on top of dataframes. You will get to see the two compared to help you know when to pick one over the other.
You will get to see a classification model built using Decision Trees the old way, and see how you can implement the same model on the newer spark.ml library.
The course covers many features of Spark 2, including going over a brand new feature in Spark 2, the ML pipelines used to chain your data transformations and ML operations.
At the end of this course you will be comfortable using the advanced features that Spark 2 offers for machine learning. You'll learn to use components such as Transformers, Estimators, and Parameters within your ML pipelines to work with distributed training at scale.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview Hi, my name is Janani Ravi and welcome to this course on Building Machine Learning Models in Spark 2. A little about myself, I have a Master's in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real-time collaborative editing in Google Docs and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. In this course, you'll learn to build and train ML models, such as regression, classification, clustering, and recommendation systems on Spark 2's distributed processing environment. This course starts off with an introduction of the two ML libraries available in Spark 2, the older spark. mllib library built on top of RDDs, and the newer spark. ml library built on top of data fields. We'll compare and contrast the two and talk about when we would choose one library over the other. This course covers both supervised and unsupervised machine learning models, starting off with classification and regression models. We'll cover decision trees and random forests for classification and Lasso and Ridge models for regression. We'll also see how we can use the confusion matrix and measures such as position and recall to see how good our classification models are. We'll also cover a brand-new feature in Spark 2, the ML pipelines, used to chain our data transformations and ML operations. We'll cover mean-shift clustering and dimensionality reduction using PCA in the unsupervised learning techniques before we move onto recommendation systems using the alternate least squared method. We'll implement a recommendations engine using both explicit, as well as implicit ratings. At the end of this course, you'll be very comfortable using the advanced features that Spark 2 offers for machine learning. You'll learn to use components such as transformers, estimators, and parameters within your ML pipelines to work with distributed training at scale.