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Any Graduates, Freshers, Engineers, Professionals, Tech Enthusiasts, or Entrepreneurs.
EdifyPath offers top-notch professional courses like PySpark. We bring your subject experts to give you a great learning experience. We come up with a strategic framework of study that can be practically applied in the real day world. We empower techies to achieve more than what they can because our courses are designed with high standards.
We all know that the enterprise industry is innovating focussed products and services which are chiefly driven by data in the backend. This resulted in causing more demand for areas of study such as Machine Learning. This Machine Learning is used to produce predictive insights, build up personalized recommendations, and many more.Since the inception of the internet, we’ve seen the best advancements in digital technology that helps in transforming already existing business processes, technologies that capture and store, competitive hardware prices to store huge volumes and varied data. In this juncture, traditional single-node data science tools such as R programming and Python fall short to scale up to big data. Spark proves to be the effective solution for the Data Engineers and Data Scientists as a mighty unified engine that is both faster (100 times faster than Hadoop for large-scale data processing) and convenient to use. It also helps Data Practitioners to solve machine learning problems intractably at a greater scale.
By now, we need to understand the significance of this widely used distributed machine learning framework in the industry for real-time production implementations. This course is surely an in-demand one among many.
PySpark is a specially designed course that is offered to students and professionals to master PySpark, Python for Apache Spark to scale data science, and Machine Learning on Big Data which is spread on a cluster of machines. Apache Spark is an open-sourced analytics engine that is unified for a wide range of data processing. It leverages Apache Spark SQL to write SQL queries so that Bid Data can be analyzed and processed to build high-performance scalable Machine Learning channels.You are just one step away from learning a PySpark course from industry experts. There is a huge scope for this course that works with Big Data and Machine Learning that has been effectively applied by the world’s leading companies like Google, Netflix, Yahoo!, Samsung, eBay, Amazon, Facebook, and many others. more...
We welcome Graduates, Freshers, Engineers (any stream), Professionals, Tech Enthusiasts, Entrepreneurs and those who see future in Scalable ML on Big Data Using Pyspark industry. There’s lot behind learning the science of Scalable ML on Big Data Using Pyspark, because of its vast scope and it is expected to be the most sustaining industry in the coming generations.
Understand the demand for distributed computing.
Learn an overview of Apache architecture and the concept of Spark Ml pipelines.
Familiarise with Databricks platform and gain hands-on experience on scalable implementations of standard machine learning algorithms.
Learn Apache Spark SQL, Apache Spark DataFrame API and start using them for your big data analysis projects.
Learn to perform exploratory data analysis and feature engineering at scale.
Understand how to accelerate ML flow for managing end-to-end machine learning life cycle, and apply to track of the record, compare parameters and arrive at a final champion model.
Vijay is a Data Science Associate Architect and holds a B.Tech from Amrita University, Bangalore. With an overall experience of a decade in the IT industry, he has implemented advanced analytics projects in the areas of Machine Learning, Natural Language Processing, and Computer Vision. He also leads the AIML Lab and focuses on leveraging an open-source tech stack to build cost-effective tools which accelerate the data science project implementations. He is adept at identifying opportunities to extract value from datasets and solve focused analytical problems to produce innovative solutions. His experience ranges across various domains like Insurance, Telecom, Retail, and Public Sector.