Machine Learning and Deep Learning for Smart Grids Big Data

Spark and Hadoop for distributed machine learning

What you'll learn
- Use of big data machine learning for smart grid big data. ML model training on multiple processes
- 2 weeks to complete the course
- No risks involved. Its advisable to have access to high performance computing infrastructure if possible
- 3 case studies to try the project at your end too

Requirements
- Basic python programming experience

Description
The course will commence with an introduction to smart grids and the significance of data management in the energy sector. We will discuss the fundamental concepts of big data and machine learning techniques and the importance of the Apache Spark and Hadoop frameworks in real-time processing of data. The next few sessions will focus on an overview of specific multiprocessing schemes for processing big data in the smart grid infrastructure.

Additionally, the course would include a significant focus on analyzing distributed real-time data using the Apache Spark framework. The course tackles big data problems for machine learning in production using Hadoop to implement distributed computation. Students will gain an in-depth understanding of Hadoop and Spark, including their usage for parallel processing of large data sets.

Moreover, the course also covers three case studies demonstrating the practical application of big data and machine learning. Students will also get hands-on experience with Apache Spark-based programs, covering distributed computing setup, program writing, and using complex algorithms for use cases.

At the end of this course, students would possess the skills required to manage big data in the energy sector efficiently, including the application of machine learning techniques and Apache Spark and Hadoop frameworks. Students will also be able to handle use cases that can be encountered in the industry.

Who this course is for:
- ML enthusiasts looking for innovative use cases
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