About this Course

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Shareable Certificate
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Advanced Level

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Approx. 10 hours to complete
English

What you will learn

  • Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle.

  • Understand how performance on a small set of disproportionately important examples may be more crucial than performance on the majority of examples.

  • Solve problems for structured, unstructured, small, and big data. Understand why label consistency is essential and how you can improve it.

Skills you will gain

Human-level Performance (HLP)Concept DriftModel baselineProject Scoping and DesignML Deployment Challenges
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Advanced Level

• Some knowledge of AI / deep learning • Intermediate Python skills • Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)

Approx. 10 hours to complete
English

Offered by

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DeepLearning.AI

Syllabus - What you will learn from this course

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Week
1

Week 1

3 hours to complete

Week 1: Overview of the ML Lifecycle and Deployment

3 hours to complete
9 videos (Total 81 min), 3 readings, 2 quizzes
Week
2

Week 2

3 hours to complete

Week 2: Select and Train a Model

3 hours to complete
16 videos (Total 108 min), 1 reading, 2 quizzes
Week
3

Week 3

4 hours to complete

Week 3: Data Definition and Baseline

4 hours to complete
16 videos (Total 128 min), 3 readings, 2 quizzes

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About the Machine Learning Engineering for Production (MLOps) Specialization

Machine Learning Engineering for Production (MLOps)

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