DSCI-272: Predicting With Cloudera Machine Learning Course
- Duration: 32 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The DSCI-272: Predicting With Cloudera Machine Learning course is designed to equip data scientists, ML engineers, and analytics professionals with the skills to build, train, and deploy predictive models using Cloudera Machine Learning (CML). This course focuses on leveraging CML tools and workflows to perform end-to-end machine learning tasks including data exploration, feature engineering, model training, evaluation, and deployment. Participants will learn practical techniques for scaling models, monitoring performance, and integrating predictive analytics into data-driven business solutions.
Course Details
- Comprehensive understanding of predictive modeling and ML lifecycle
- Hands-on experience with Cloudera Machine Learning (CML) platform
- Ability to preprocess data, engineer features, and train models at scale
- Skills to evaluate, tune, and deploy machine learning models
- Knowledge of monitoring and maintaining models in production
- Streamlined preparation for Cloudera machine learning roles
- Open to data scientists, ML engineers, analytics professionals, and developers
- Ideal for professionals involved in predictive analytics and data modeling
- Basic understanding of Python programming and statistics is recommended
- Familiarity with machine learning concepts and workflows is beneficial
- Corporate sponsorship or group participation is encouraged
- Master the fundamentals of predictive modeling and machine learning workflows
- Learn to use Cloudera Machine Learning to build scalable ML pipelines
- Gain expertise in feature engineering and model evaluation
- Understand how to deploy models and monitor their performance in production
- Apply practical machine learning techniques to real-world use cases
- Achieve readiness for machine learning roles with hands-on CML experience
- Introduction to Cloudera Machine Learning (CML)
- ML Workflow: Data Preparation & Exploration
- Feature Engineering Techniques
- Training Machine Learning Models with CML
- Model Evaluation and Tuning
- Deployment Strategies and Model Serving
- Monitoring and Maintaining Models in Production
- CML Integration with Data Pipelines
- Real-World Predictive Analytics Use Cases
- Hands-On Labs and Practical ML Exercises
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