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DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning ) Course Overview

DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning ) Course Overview

DSCI-272: Predicting with Cloudera Machine Learning offers an immersive four-day instructor-led training experience tailored for intermediate learners engaged in Enterprise data science. Through this course, participants will master the end-to-end Machine learning workflows using Cloudera Machine Learning (CML) on the Cloudera Data Platform (CDP). You'll tackle hands-on exercises to Explore, visualize, and analyze data, and also, importantly, train, evaluate, and deploy machine learning models. The practical application focuses on using Python and PySpark within CML environments, ensuring learners can effectively manage and Deploy ML models as a REST API, preparing them for real-world data science challenges.

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  • Live Training (Duration : 32 Hours)
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Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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Target Audience for DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )

DSCI-272: Predicting with Cloudera Machine Learning is an intermediate level course designed to equip data professionals with the skills to develop and deploy machine learning workflows using Cloudera's CML and CDP.


• Data Scientists
• Data Engineers
• Machine Learning Engineers
• Developers involved in data science projects
• Solution Architects
• IT Professionals working with big data and machine learning platforms
• Analytics Professionals seeking to leverage Cloudera's machine learning tools




Learning Objectives - What you will Learn in this DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )?

Introduction to the Learning Outcomes of DSCI-272: Predicting with Cloudera Machine Learning This course equips participants with the skills to create, evaluate, and deploy machine learning models using Cloudera Machine Learning on the Cloudera Data Platform.

Learning Objectives and Outcomes:

  • Understand and utilize Cloudera SDX and other Cloudera Data Platform components for locating and managing data suitable for machine learning experiments.
  • Employ Apache Spark and Spark ML for conducting scalable data analysis and machine learning on big datasets.
  • Execute full machine learning workflows, including data preparation, model training, and model evaluation within the CML environment.
  • Deploy machine learning models as REST APIs, allowing for integration into broader application ecosystems.
  • Effectively manage and monitor machine learning models post-deployment to ensure performance and accuracy.
  • Use the Applied ML Prototypes (AMPs) to jump-start machine learning projects with pre-built solutions.
  • Connect to various data source types within Cloudera Machine Learning for comprehensive data exploration and visualization.
  • Manipulate and transform data using DataFrame operations in PySpark within the Cloudera ecosystem.
  • Address complex data types and apply user-defined functions in Spark to enhance data analysis capabilities.
  • Master MLlib in Spark for executing machine

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