Published 05/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 17 lectures (2h 36m) | Size: 866.6MB
Covering Computer Vision Problems and General ML problems in an interactive way
What you’ll learn
Machine Learning System Design Interviews
Machine Learning Pipeline
Concepts to start learning more behind them
Interactive interviewer-interviewee dialogue
Requirements
Classical Machine Learning Background
Deep Learning Background
Built a few non-trivial ML projects
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
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
Overall, this is an intermediate level course. Beginners shouldn’t start with this course
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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