Machine Learning System Design Interviews: Get Skilled

Udemy Machine Learning System Design Interviews: Get Skilled

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Covering Computer Vision Problems and General ML problems in an interactive way

Description​

When we start learning Machine Learning, our main focus is building the model! The data usually is clean and ready. The task usually is a simple classifier or regressor. We keep learning several models and the math behind them!

In reality, we need to formulate the problem as a machine learning problem! We need data and the corresponding annotations. Most probably we need to do a lot of cleaning, preprocessing and visualizing the data. And then comes the model! A missing stage for many people is deploying the model and integrating it with a product!

In this course, we focus on highlighting all the machine learning pipeline:
  • Scoping the problem
  • Data: collection and annotation
  • Metrics: online and offline
  • Modeling
  • Evaluation
  • Deploying

What to expect in this course:
  • To emphasize the machine learning pipeline, not just the modeling!
  • To get deep insights about what does it mean to build a ML system!
  • A good reference of questions to ask for yourself in your projects
  • To prepare for the ML system design interviews!
    • This is actually the major concern and what drives the content
  • An interactive content: Question and Answer

Content:
  • A few general ML systems with good details coverage
  • A few Computer vision systems with good details coverage
  • Course is under-progress

Audience
  • If you don’t know machine learning, this course is not for you
  • If you just build toy ML projects, this course may not be for you
  • If you build some projects or non-trivial Kaggle competitions, this course is for you
  • If you build have market experience, this course is a must for you

Critical notes:
  • Don’t take my thoughts for granted. Challenge them. Brainstorm in the QA section.
  • I don't explain machine learning concepts. I highlight them. It is your responsibility.
    • You will be exposed to a wide range of terminologies

About the Instructor (relevant experience): I have started worked in machine learning since 2010. I am a Computer Vision Scientist with PhD from Simon Fraser University. My experience covers many areas such as algorithms design, software engineering, machine learning and teaching.

Don't miss such a unique learning experience!

Acknowledgement: “I’d like to extend my gratitude towards Robert Bogan for his help with proofreading the slides for this course”

Who this course is for:​

  • someone would like to move toward the ML pipeline
  • someone would like to prepare for Machine Learning System Design Interviews
  • someone in the market and would like to enhance the big picture
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TUTProfessor
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