Machine Learning for Ms Students
Course Curriculum
- Course overview, goals, and expected outcomes
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- Course overview, goals, and expected outcomes 3
- Course overview, goals, and expected outcomes 1
- Course overview, goals, and expected outcomes 2
- Problem formulation, success metrics, and decision framing
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- Problem formulation, success metrics, and decision framing 1
- Problem formulation, success metrics, and decision framing 3
- Problem formulation, success metrics, and decision framing 2
- Data lifecycle: collection, labeling, and quality control
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- Data lifecycle: collection, labeling, and quality control 3
- Data lifecycle: collection, labeling, and quality control 1
- Data lifecycle: collection, labeling, and quality control 2
- Experimentation, reproducibility, and versioning
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- Experimentation, reproducibility, and versioning 2
- Experimentation, reproducibility, and versioning 1
- Experimentation, reproducibility, and versioning 3
- Project road-map: prototyping, validation, and deployment
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- Project road-map: prototyping, validation, and deployment 2
- Project road-map: prototyping, validation, and deployment 1
- Project road-map: prototyping, validation, and deployment 3
- Probability theory fundamentals and common distributions
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- Probability theory fundamentals and common distributions 1
- Probability theory fundamentals and common distributions 2
Instructor
Mahmudul Hasan
Course Instructor
Education Center