Machine & Deep Learning – Practitioner
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Course Overview
Course Overview:
At the Practitioner level, students learn how to:
Design machine learning pipelines
Select appropriate algorithms for different problem types
Optimize models for performance and reliability
Introduce neural networks and deep learning foundations
Apply ML techniques to real-world use cases
The course is project-driven, with continuous guided practical sessions and a comprehensive capstone project.
Learning Peeks
Course Duration
3 Months
Course Structure
Instructor-Led
Availability
Available Online/Offline
Flexible Schedules
Flexible study schedules
Language
English Language
Training Days
Monday, Wednesday, and Friday
Recognized Certification
Earn a certification on completion
Course Outcome
Course Outcomes:
By the end of this course, learners will be able to:
Build and compare multiple ML models for a single problem
Apply feature engineering and feature selection techniques
Use ensemble learning methods
Implement unsupervised learning algorithms
Train and evaluate basic neural networks
Apply model tuning and validation strategies
Handle real-world datasets with noise and imbalance
Present ML solutions with technical justification
Prepare ML projects for advanced-level deployment concepts
A World-Class Learning Facility
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Our classrooms are carefully designed to facilitate effective teaching and learning to enable tutors to deliver dynamic and engaging lessons that captivate students attention and spark their curiosity.
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