Machine Learning System Design Interviews: Get Skilled

当我们开始学习机器学习时，我们的主要重点是建立模型！数据通常是干净且准备好的。该任务通常是一个简单的分类器或回归器。我们不断学习几种模型及其背后的数学原理！

实际上，我们需要将问题表述为机器学习问题！我们需要数据和相应的注释。很可能我们需要对数据进行大量清理、预处理和可视化。然后是模型！许多人缺少的阶段是部署模型并将其与产品集成！

在本课程中，我们将重点介绍所有机器学习管道

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