Hello friends, we are here again today for another exciting topic to discuss. But, today we are not gonna discuss something which is related to Java or any other language or spring boot. Today we are gonna discuss something which is immensely practical and has the potential to land you very high-paying data science jobs. Today we are gonna review a course that focuses on Machine Learning! Machine Learning is very important when we are considering data science interviews! So what's the wait? Let's start! On Educative.io, there is a great course called Grokking the Machine Learning Interview. It couldn't have come at a better moment, with machine learning expected to be a $3.6 billion business by 2024.
- machine-learning comprehension
- design of a machine learning system
- problem-solving and coding
"Narrow" questions that assess your knowledge of basic machine
learning topics such as bias and variance, supervised vs.
unsupervised learning, Bayes' theorem, and so on. The point of this
exercise is to see if you truly know your way around ML. These
principles can be learned from a variety of sources.
ML system design problems that are "open-ended." For example, you
may be asked how you would go about developing an ad prediction
system, a search ranking system, or a social media newsfeed. The
objective is to see if you're capable of "zooming out" and thinking
about systems on a larger scale.
Can you consider the benefits and drawbacks of various techniques and explain your thoughts clearly? Apart from Grokking the Machine Learning Interview, there are no other materials I've seen that teach you how to approach machine learning system design interview questions.
The authors' major objective was to provide enough material for learners to think through and make ML system design decisions for the kind of open-ended questions they're likely to encounter during their interviews. It assists students in considering requirements, comparing alternatives, and developing solutions that they can confidently defend.
Senior ML engineers with industry experience, notably at FAANG firms, designed and peer-reviewed this course. These are engineers who have spent almost as long tackling real-world ML issues as they have to interview other ML engineers. It went through hundreds of changes and comments over the course of a year to arrive at its current state.
Is Grokking the Machine Learning Interview on Educative Worth it?
- Needs for scale and latency
- architecting for scale defining metrics
- Iterative model improvement through offline development and execution
- capacity and performance
- transferring knowledge
- Model testing and debugging
- experimenting on the internet
- data gathering techniques for training
- Self-Driving Car: Image Segmentation
- Feed Based System
- Recommendation System
- Entity Linking System
- Ad Prediction System
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