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NOTE: This course is currently replaced with IBM Machine Learning with Apache Spark.
Further your data engineering career with this self-paced course about machine learning with Apache Spark! Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
In this short course you'll gain these practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering.
In this course you will learn about data sources, streaming output modes, and supported data destinations. You will gain insights about the advantages of Apache Spark GraphFrames and complete a number of hands-on labs to apply your knowledge.
You will then move on to learning about machine learning using SparkML, the Spark Machine Learning library. You will gain an understanding of both supervised and unsupervised machine learning, classification and regression tasks, as well as clustering.
The course ends with a final project where you will create your own Apache Spark application for performing Extract, Transform, and Load (ETL) processes.
NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or have skills similar to the ones learnt in that course.
Further your data engineering career with this self-paced course about machine learning with Apache Spark! Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
In this short course you'll gain these practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering.
In this course you will learn about data sources, streaming output modes, and supported data destinations. You will gain insights about the advantages of Apache Spark GraphFrames and complete a number of hands-on labs to apply your knowledge.
You will then move on to learning about machine learning using SparkML, the Spark Machine Learning library. You will gain an understanding of both supervised and unsupervised machine learning, classification and regression tasks, as well as clustering.
The course ends with a final project where you will create your own Apache Spark application for performing Extract, Transform, and Load (ETL) processes.
NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or have skills similar to the ones learnt in that course.
